{
  "metadata": {
    "version": "2.0",
    "date": "2026-02-06",
    "langue": "fr",
    "description": "Métiers Data/IA avec compétences et outils enrichis 2025-2026"
  },
  "metiers": [
    {
      "id": "data_analyst",
      "nom": "Data Analyst",
      "categorie": "Noyau data & analytique",
      "description": "Analyse les données pour produire des rapports, tableaux de bord et recommandations opérationnelles au service des métiers.",
      "missions_principales": [
        "Collecter, nettoyer et structurer les données issues de différentes sources",
        "Construire et maintenir des tableaux de bord et rapports réguliers",
        "Analyser les tendances, identifier des anomalies et opportunités",
        "Traduire les besoins métier en indicateurs et analyses",
        "Présenter les résultats et recommandations aux équipes métier"
      ],
      "competences_techniques": [
        "Solide maîtrise de SQL (jointures, agrégations, CTE, window functions de base)",
        "Bonne maîtrise d’Excel/Google Sheets (formules avancées, tableaux croisés dynamiques, macros simples)",
        "Visualisation de données avec un ou plusieurs outils BI",
        "Manipulation et préparation de données avec Python (pandas) ou R pour les profils plus techniques",
        "Compréhension des bases de données relationnelles et schémas classiques (étoile, snowflake)",
        "Notions de statistiques descriptives (moyenne, médiane, variance, corrélations, tests simples)",
        "Connaissance de base des data warehouses et data lakes",
        "Bonnes pratiques de requêtage (performance, lisibilité, documentation)",
        "Notions d’automatisation des rapports (scheduling, rafraîchissement des datasets)",
        "Capacité à interpréter les résultats d’AB testing ou d’expérimentations simples"
      ],
      "outils_technologies": [
        "SQL (PostgreSQL, MySQL, SQL Server, BigQuery, Snowflake)",
        "Excel, Google Sheets",
        "BI : Power BI, Tableau, Looker, Qlik",
        "Python (pandas, numpy) ou R (dplyr, ggplot2) pour les profils plus techniques",
        "Outils de data warehouse : BigQuery, Snowflake, Redshift (en consommation)",
        "Outils de collaboration : Confluence, Notion, Jira",
        "Git en lecture / basique pour récupérer des scripts ou requêtes partagées"
      ],
      "competences_soft": [
        "Capacité à comprendre rapidement les besoins métier",
        "Clarté dans la communication écrite et orale",
        "Esprit de synthèse et sens de la pédagogie",
        "Rigueur et attention aux détails",
        "Capacité à challenger les chiffres et à détecter des incohérences"
      ],
      "niveau_etude": "Bac+3 à Bac+5 (licence pro, bachelor, Master en statistiques, économie, informatique décisionnelle ou école de commerce avec majeure data/BI).",
      "formations_utiles": [
        "Licence / Master en statistiques, MIAGE, économie quantitative",
        "Master ou mastère spécialisé en Business Intelligence ou data analytics",
        "Bootcamps data analytics (3–6 mois)",
        "Certifications Power BI, Tableau, Looker"
      ],
      "experience_requise": "0–2 ans pour un profil junior, 3–5 ans pour un profil confirmé pouvant piloter un périmètre métier.",
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    },
    {
      "id": "bi_analyst",
      "nom": "Business Intelligence Analyst",
      "categorie": "Noyau data & analytique",
      "description": "Conçoit et administre des solutions décisionnelles (BI) pour fournir aux métiers une vision fiable et partagée des indicateurs clés.",
      "missions_principales": [
        "Recueillir les besoins des métiers en matière de reporting et d’indicateurs",
        "Concevoir et développer des tableaux de bord dynamiques et interactifs",
        "Modéliser les données pour le décisionnel (schémas en étoile, cubes OLAP)",
        "Optimiser les requêtes et sources pour garantir de bonnes performances",
        "Administrer les plateformes BI (droits, rafraîchissements, gouvernance des reports)"
      ],
      "competences_techniques": [
        "Maîtrise des concepts BI (dimensions, faits, grain, mesures, KPI)",
        "SQL avancé appliqué au décisionnel",
        "Conception de schémas en étoile et modèles analytiques",
        "Utilisation avancée d’un ou plusieurs outils BI (DAX, MDX, calculs complexes)",
        "Gestion des sources de données, connexions, gateways et rafraîchissement",
        "Notions de performance tuning sur requêtes et modèles BI",
        "Compréhension des principes de gouvernance BI (catalogue de rapports, validation, cycles de vie)"
      ],
      "outils_technologies": [
        "Power BI (DAX, Power Query), Tableau, Looker, Qlik",
        "SQL Server, Oracle, PostgreSQL",
        "Outils ETL/ELT simples (Power Query, Fivetran, Stitch, Talend dans certains environnements)",
        "Outils de gestion de versions de rapports / assets BI (Git, workspaces managés)"
      ],
      "competences_soft": [
        "Excellente compréhension des besoins métier et de la chaîne de décision",
        "Capacité à prioriser les demandes de reporting",
        "Sens de la pédagogie pour former les utilisateurs aux outils BI",
        "Communication claire avec les équipes IT et métiers"
      ],
      "niveau_etude": "Bac+3 à Bac+5 en informatique décisionnelle, statistiques, école d’ingénieur/commerce avec spécialisation BI.",
      "formations_utiles": [
        "Master ou mastère spécialisé en Business Intelligence / décisionnel",
        "Formations éditeur (Power BI, Tableau, Qlik)",
        "Certifications officielles des outils BI"
      ],
      "experience_requise": "1–3 ans dans le décisionnel ou la data pour être opérationnel en autonomie.",
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    },
    {
      "id": "data_miner",
      "nom": "Data Miner",
      "categorie": "Noyau data & analytique",
      "description": "Utilise des techniques de fouille de données pour découvrir des patterns, segments et relations cachées dans de grands volumes de données.",
      "missions_principales": [
        "Préparer et explorer de grands volumes de données",
        "Mettre en œuvre des méthodes de segmentation, scoring, clustering",
        "Identifier des patterns et signaux faibles pertinents pour le métier",
        "Tester et valider des hypothèses statistiques",
        "Documenter les résultats et proposer des recommandations opérationnelles"
      ],
      "competences_techniques": [
        "Statistiques descriptives et inférentielles",
        "Techniques de clustering (K-means, DBSCAN, hiérarchique, etc.)",
        "Réduction de dimension (PCA, t-SNE, UMAP)",
        "Notions de machine learning supervisé pour scoring prédictif",
        "Manipulation de données avec SQL et Python/R",
        "Visualisation avancée (cartes de chaleur, matrices de corrélation, etc.)"
      ],
      "outils_technologies": [
        "Python (pandas, scikit-learn, scipy)",
        "R (tidyverse, caret, factoextra)",
        "Outils de data mining type RapidMiner, KNIME, Orange dans certains contextes",
        "SQL pour l’extraction de données",
        "BI/visualisation pour présenter les résultats"
      ],
      "competences_soft": [
        "Curiosité analytique et capacité à formuler des hypothèses",
        "Rigueur dans la méthodologie et la validation",
        "Capacité à expliquer des patterns complexes de façon accessible"
      ],
      "niveau_etude": "Bac+3 à Bac+5 en statistiques, data science, mathématiques appliquées.",
      "formations_utiles": [
        "Licence/Master en statistiques ou data science",
        "Formations data mining, segmentation, scoring marketing",
        "MOOC spécialisés en data mining et clustering"
      ],
      "experience_requise": "1–3 ans d’expérience en analyse de données ou data science.",
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    {
      "id": "statistician",
      "nom": "Statisticien Data",
      "categorie": "Noyau data & analytique",
      "description": "Conçoit des plans d’échantillonnage et des modèles statistiques pour analyser et interpréter les données de manière rigoureuse.",
      "missions_principales": [
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        "Choisir et appliquer les méthodes statistiques appropriées",
        "Valider la qualité des données et la robustesse des résultats",
        "Produire des rapports et avis statistiques pour éclairer les décisions",
        "Collaborer avec des data scientists et métiers sur la méthodologie"
      ],
      "competences_techniques": [
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        "Tests d’hypothèses, intervalles de confiance, ANOVA",
        "Modèles de régression (linéaire, logistique, Poisson, etc.)",
        "Méthodes bayésiennes de base",
        "Logiciels statistiques (R, SAS, éventuellement Stata/SPSS)",
        "Programmation en R ou Python pour l’automatisation d’analyses",
        "Gestion de données d’enquête et pondérations"
      ],
      "outils_technologies": [
        "R (tidyverse, lme4, brms, etc.)",
        "SAS, SPSS, Stata selon les secteurs",
        "Python (pandas, statsmodels, scipy)",
        "SQL pour l’accès aux données",
        "LaTeX ou RMarkdown pour la production de rapports"
      ],
      "competences_soft": [
        "Rigueur scientifique et sens du détail",
        "Capacité à challenger la qualité des données et les hypothèses",
        "Bonne communication avec des non-statisticiens"
      ],
      "niveau_etude": "Bac+5 minimum (Master en statistiques, mathématiques appliquées) ; Doctorat apprécié dans certains domaines (santé, recherche, R&D).",
      "formations_utiles": [
        "Master en statistiques ou biostatistiques",
        "Doctorat en statistiques / mathématiques appliquées",
        "Formations spécialisées selon le domaine (santé, économie, industrie)"
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    },
    {
      "id": "data_scientist",
      "nom": "Data Scientist",
      "categorie": "Noyau data & analytique",
      "description": "Conçoit des modèles prédictifs, explore les données et construit des solutions ML/IA pour répondre à des problématiques métier complexes.",
      "missions_principales": [
        "Réaliser l'exploration et la préparation de données sur différentes sources et formats",
        "Concevoir, entraîner, évaluer et comparer des modèles de machine learning et deep learning",
        "Traduire des besoins métier en problématiques data et en indicateurs mesurables",
        "Industrialiser des prototypes en scripts ou jobs reproductibles",
        "Collaborer avec les équipes data engineering, produit et métier",
        "Communiquer les résultats via visualisations, rapports et data storytelling"
      ],
      "competences_techniques": [
        "Python avancé (pandas, numpy, scipy, scikit-learn, statsmodels)",
        "Programmation orientée objet, bonnes pratiques (tests unitaires, packaging, logging, CI)",
        "Statistiques avancées (tests d’hypothèses, échantillonnage, bootstrap, méthodes bayésiennes de base)",
        "Machine learning supervisé et non supervisé (régression, arbres, gradient boosting, SVM, clustering, réduction de dimension)",
        "Deep learning (CNN, RNN, transformers) pour texte, image et séries temporelles",
        "Traitement de données à grande échelle avec Spark / PySpark ou Dask",
        "Feature engineering, sélection de variables, encoding, normalisation, gestion des valeurs manquantes",
        "Évaluation de modèles (cross-validation, métriques de classification et régression, AUC, F1, courbes PR, calibration)",
        "Concepts de MLOps de base (suivi d’expériences, versioning de modèles et de données, reproductibilité)",
        "Cloud data & AI (AWS, GCP ou Azure) pour entraînement et déploiement",
        "Bon niveau SQL (jointures complexes, CTE, window functions, optimisation)",
        "Connaissances de base en LLMs et GenAI (APIs, RAG simple, prompt engineering de base)"
      ],
      "outils_technologies": [
        "Python, SQL, éventuellement R",
        "scikit-learn, XGBoost, LightGBM, CatBoost",
        "TensorFlow, Keras, PyTorch, Hugging Face Transformers",
        "Apache Spark / PySpark, Dask",
        "pandas, polars",
        "Matplotlib, Seaborn, Plotly, Power BI, Tableau",
        "MLflow, Weights & Biases, DVC",
        "JupyterLab, VS Code, Databricks, Vertex AI Workbench, SageMaker Studio",
        "PostgreSQL, MySQL, BigQuery, Snowflake, MongoDB",
        "Git, GitHub/GitLab, GitHub Actions / GitLab CI"
      ],
      "competences_soft": [
        "Capacité à vulgariser des résultats techniques à des publics non techniques",
        "Orientation produit et impact business",
        "Esprit critique et rigueur scientifique",
        "Curiosité et veille technologique continue",
        "Travail en équipe, pair programming ponctuel, feedback constructif"
      ],
      "niveau_etude": "Bac+5 (Master data science, statistiques, mathématiques appliquées ou école d’ingénieur) ; Doctorat apprécié pour des postes orientés R&D.",
      "formations_utiles": [
        "Master Data Science / IA",
        "École d’ingénieur avec majeure data/IA",
        "Certifications cloud orientées data/ML (AWS ML Specialty, GCP Professional ML Engineer, Azure DP-100)",
        "MOOC/bootcamps avancés en machine learning, deep learning, NLP et MLOps"
      ],
      "experience_requise": "0–2 ans pour junior, 3–5 ans pour confirmé, 6+ ans pour senior / lead.",
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    {
      "metiers": [
        {
          "id": "data_engineer",
          "nom": "Data Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Construit et maintient les pipelines et plateformes de données modernes (batch et streaming) pour alimenter l’analytics, le reporting et les systèmes d’IA.",
          "missions_principales": [
            "Concevoir et implémenter des architectures de données (data lake, data warehouse, lakehouse)",
            "Mettre en place des pipelines ELT/ETL robustes, monitorés et testés",
            "Gérer l’ingestion de données en batch et temps réel (streaming)",
            "Optimiser les performances, la disponibilité et les coûts des plateformes data, notamment dans le cloud",
            "Collaborer avec data scientists, analysts et équipes produit pour exposer des données consommables",
            "Mettre en œuvre des pratiques de qualité, sécurité et gouvernance des données"
          ],
          "competences_techniques": [
            "SQL avancé (requêtes complexes, tuning, indexation, partitionnement)",
            "Python pour data engineering (scripts, packages, tests, typage, logging)",
            "Modern data stack : dbt (data modeling & transformations), orchestration (Airflow, Dagster, Prefect)",
            "Streaming & messaging : Apache Kafka, Confluent, AWS Kinesis, GCP Pub/Sub",
            "Traitement distribué : Apache Spark (batch & streaming), éventuellement Flink ou Beam",
            "Cloud data warehouses : Snowflake, BigQuery, Redshift, Synapse",
            "Stockage data lake : S3, GCS, ADLS ; formats Parquet, ORC, Delta Lake, Apache Iceberg",
            "CI/CD & DevOps : Git, pipelines CI, conteneurisation (Docker), infrastructure as code (Terraform, CloudFormation, Pulumi)",
            "Concepts d’architecture data (lambda, kappa, lakehouse, microservices data)",
            "Notions de sécurité : IAM, chiffrement au repos/en transit, gestion des secrets, data masking",
            "Tests de données : tests unitaires, de contrats et de qualité (schémas, contraintes, anomalies)"
          ],
          "outils_technologies": [
            "Orchestration : Apache Airflow, Dagster, Prefect",
            "Transformation : dbt (core ou cloud)",
            "Streaming : Kafka, Confluent Platform, AWS Kinesis, GCP Pub/Sub",
            "Traitement : Spark (Databricks, EMR, DataProc), Flink, Beam",
            "Cloud : AWS (Glue, Redshift, S3), GCP (Dataflow, BigQuery, GCS), Azure (Data Factory, Synapse, ADLS)",
            "Data warehouse : Snowflake, BigQuery, Redshift, Synapse",
            "Monitoring : Prometheus, Grafana, services de monitoring cloud natif",
            "Infra as code : Terraform, CloudFormation, Pulumi",
            "Conteneurs : Docker, Kubernetes (EKS, GKE, AKS)",
            "Qualité & catalogue : Great Expectations, Soda Core, OpenMetadata, Amundsen"
          ],
          "competences_soft": [
            "Orientation fiabilité et résilience (SRE mindset appliqué aux données)",
            "Capacité à collaborer avec data scientists, analysts, product et infra",
            "Documentation claire des architectures et pipelines",
            "Analyse de risques (SLA/SLO, volumétrie, coûts cloud, dette technique)",
            "Autonomie et capacité à prioriser les chantiers structurants"
          ],
          "niveau_etude": "Bac+3 à Bac+5 (licence pro, Master, école d’ingénieur en informatique, systèmes distribués ou data).",
          "formations_utiles": [
            "Master Big Data / Systèmes distribués / Cloud",
            "Bootcamps ou formations intensives en data engineering et modern data stack",
            "Certifications cloud data (AWS Data Analytics, GCP Professional Data Engineer, Azure DP-203)",
            "Formations spécialisées Kafka, Spark, dbt, Airflow/Dagster"
          ],
          "experience_requise": "1–2 ans pour profils juniors, 3–5 ans pour confirmé, 5+ ans pour data architect.",
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        },
        {
          "id": "analytics_engineer",
          "nom": "Analytics Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Fait le pont entre data engineering et data analytics : modèle les données pour les rendre directement exploitables par les analystes et les métiers.",
          "missions_principales": [
            "Modéliser les données analytiques (modèles dimensionnels, marts orientés métier)",
            "Écrire et maintenir les transformations SQL (ELT) dans le data warehouse",
            "Documenter les modèles de données et les définitions de KPI",
            "Collaborer avec les data analysts pour répondre rapidement aux besoins métier",
            "Assurer la qualité, la cohérence et la performance des datasets analytiques"
          ],
          "competences_techniques": [
            "SQL avancé (window functions, CTE, optimisation)",
            "Modélisation de données BI (étoile, snowflake, data vault simplifié)",
            "Transformation ELT dans un data warehouse cloud",
            "Utilisation avancée de dbt (models, tests, documentation, lineage)",
            "Notions de performance des requêtes et des schémas dans DW cloud",
            "Compréhension des besoins métier et des métriques business",
            "Notions de Git et CI pour les projets analytics"
          ],
          "outils_technologies": [
            "dbt (core/cloud)",
            "Data warehouses : Snowflake, BigQuery, Redshift, Synapse",
            "SQL (PostgreSQL, BigQuery, Snowflake)",
            "Orchestration : Airflow, Dagster, Prefect (en collaboration avec data engineers)",
            "BI : Power BI, Looker, Tableau (en consommation et préparation de modèles)",
            "Git, GitHub/GitLab pour versionner le code analytics"
          ],
          "competences_soft": [
            "Forte sensibilité business",
            "Communication efficace avec les métiers et les analystes",
            "Rigueur dans la documentation et la définition de KPI",
            "Capacité à prioriser les demandes analytics"
          ],
          "niveau_etude": "Bac+3 à Bac+5 (informatique décisionnelle, statistiques, école d’ingénieur ou commerce avec spécialisation data).",
          "formations_utiles": [
            "Formations modern data stack (dbt, Snowflake/BigQuery, Looker/Power BI)",
            "Bootcamps data analytics / analytics engineering",
            "Certifications sur data warehouses cloud"
          ],
          "experience_requise": "1–3 ans en data analysis/BI ou data engineering.",
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        },
        {
          "id": "dataops_engineer",
          "nom": "DataOps Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Industrialise et automatise les processus data (du sourcing à la consommation) en appliquant les principes DevOps au monde de la donnée.",
          "missions_principales": [
            "Automatiser le déploiement et la surveillance des pipelines de données",
            "Mettre en place des tests et contrôles qualité sur les données",
            "Gérer les environnements (dev, test, prod) pour les workflows data",
            "Superviser les SLA/SLO des flux et agir en cas d’incidents",
            "Collaborer avec data engineers, analysts et SRE pour fiabiliser la plateforme"
          ],
          "competences_techniques": [
            "Bon niveau en Python et/ou bash pour l’automatisation",
            "Orchestration de workflows (Airflow, Dagster, Prefect)",
            "CI/CD (GitHub Actions, GitLab CI, Jenkins)",
            "Monitoring & alerting (Prometheus, Grafana, outils cloud natifs)",
            "Concepts DataOps (tests de données, data contracts, observabilité des données)",
            "Connaissance des architectures data et des principaux outils de la stack data",
            "Notions de conteneurisation et Kubernetes"
          ],
          "outils_technologies": [
            "Airflow, Dagster, Prefect",
            "Git, GitHub/GitLab, Jenkins",
            "Prometheus, Grafana, Loki, outils de logs",
            "Great Expectations, Soda pour la qualité des données",
            "Docker, Kubernetes",
            "Outils de ticketing (Jira) et de gestion d’incidents"
          ],
          "competences_soft": [
            "Culture fiabilité / SRE appliquée aux données",
            "Capacité à collaborer avec plusieurs profils (ingénieurs, ops, métier)",
            "Rigueur, sens du détail, gestion de l’urgence en cas d’incident"
          ],
          "niveau_etude": "Bac+3 à Bac+5 en informatique, systèmes, data engineering.",
          "formations_utiles": [
            "Formations DevOps/Cloud complétées par un volet data",
            "Certifications cloud (AWS, GCP, Azure)",
            "Formations DataOps, Data Observability"
          ],
          "experience_requise": "2–4 ans en data engineering, DevOps ou SRE.",
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        },
        {
          "id": "mlops_engineer",
          "nom": "MLOps Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Automatise et fiabilise le cycle de vie des modèles de machine learning (training, déploiement, monitoring, retraining) en production.",
          "missions_principales": [
            "Concevoir et maintenir des pipelines de training, validation et déploiement des modèles",
            "Intégrer le tracking d’expériences, de datasets et de modèles dans les workflows",
            "Mettre en place le monitoring des modèles (performance, dérive, incidents)",
            "Automatiser le retraining et la mise à jour des modèles",
            "Collaborer avec data scientists, ML engineers et équipes infra/DevOps"
          ],
          "competences_techniques": [
            "Solide base DevOps (Linux, réseaux, CI/CD, conteneurs, Kubernetes)",
            "Connaissance pratique des frameworks ML (scikit-learn, TensorFlow, PyTorch)",
            "MLOps : experiment tracking, model registry, feature store, pipelines ML",
            "Outils de déploiement ML (batch, API temps réel, streaming)",
            "Monitoring de modèles : dérive, performance, fairness, logs",
            "Cloud AI : SageMaker, Vertex AI, Azure ML, Databricks ML",
            "Notions de sécurité et conformité spécifique à l’IA (audit, traçabilité)"
          ],
          "outils_technologies": [
            "MLflow, Kubeflow, ZenML, Metaflow",
            "Weights & Biases, Neptune.ai",
            "CI/CD : GitHub Actions, GitLab CI, Argo CD",
            "Serving : FastAPI, TorchServe, TF Serving, Triton, BentoML",
            "Infrastructure : Docker, Kubernetes, Helm",
            "Monitoring : Prometheus, Grafana, outils de model monitoring (Evidently AI, WhyLabs)",
            "Cloud AI : AWS SageMaker, GCP Vertex AI, Azure ML"
          ],
          "competences_soft": [
            "Culture de la fiabilité et de l’automatisation",
            "Communication avec data scientists et DevOps",
            "Capacité à standardiser les pratiques dans l’équipe",
            "Veille technologique sur l’écosystème MLOps/LLMOps"
          ],
          "niveau_etude": "Bac+3 à Bac+5 en informatique, data, ou équivalent, avec forte appétence pour l’IA et le DevOps.",
          "formations_utiles": [
            "Formations MLOps spécialisées",
            "Certifications cloud ML (AWS, GCP, Azure)",
            "Formations avancées en Kubernetes et CI/CD"
          ],
          "experience_requise": "2–5 ans en ML engineering, DevOps ou data engineering.",
          "embedding": [
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        {
          "id": "ml_engineer",
          "nom": "Machine Learning Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Met en production des modèles de ML/IA, conçoit des APIs de prédiction et gère le cycle de vie des modèles (training, déploiement, monitoring, retraining).",
          "missions_principales": [
            "Industrialiser des prototypes de data science en services robustes et scalables",
            "Concevoir des APIs de prédiction (batch, temps réel, streaming) pour les produits",
            "Mettre en place des pipelines de training, validation, déploiement et monitoring de modèles",
            "Optimiser les performances (latence, coût, consommation GPU/CPU) et la robustesse des systèmes ML",
            "Collaborer avec les équipes produit, data science, DevOps/MLOps et sécurité"
          ],
          "competences_techniques": [
            "Python avancé et bonnes pratiques d’ingénierie logicielle (tests, patterns, CI/CD, observabilité)",
            "Maîtrise de frameworks ML et deep learning (scikit-learn, TensorFlow, PyTorch)",
            "Connaissance des LLMs et transformers (fine-tuning, prompt engineering, RAG)",
            "MLOps : conception de pipelines de training et déploiement, gestion des versions de modèles et de données",
            "Déploiement de modèles : REST/gRPC, batch inference, streaming inference",
            "Optimisation : quantization, pruning, distillation, ONNX, TensorRT, optimisation GPU",
            "Monitoring de modèles (dérive de données, dérive de labels, performance en production, fairness)",
            "Cloud AI : SageMaker, Vertex AI, Azure ML, Databricks ML",
            "Bases solides en statistiques, ML classique et deep learning",
            "Notions de sécurité spécifiques à l’IA (attaques adversariales, prompt injection pour LLMs)"
          ],
          "outils_technologies": [
            "scikit-learn, XGBoost, LightGBM",
            "TensorFlow, Keras, PyTorch",
            "Hugging Face (Transformers, PEFT), LangChain, LlamaIndex, APIs OpenAI/Anthropic",
            "MLflow, Kubeflow, ZenML, DVC, Weights & Biases",
            "FastAPI, Flask, TorchServe, TensorFlow Serving, Triton Inference Server, BentoML",
            "GitHub Actions, GitLab CI, Argo CD, Tekton",
            "Docker, Kubernetes, Helm, GPU sur cloud",
            "Prometheus, Grafana, outils de model monitoring spécialisés"
          ],
          "competences_soft": [
            "Capacité à arbitrer entre performance modèle et contraintes produit (latence, coûts, maintenance)",
            "Travail rapproché avec les équipes infra, data engineering et produit",
            "Sens de la fiabilité, de l’observabilité et de l’automatisation",
            "Capacité à documenter des systèmes complexes (diagrammes d’architecture, RFC techniques, READMEs)"
          ],
          "niveau_etude": "Bac+5 (Master/école d’ingénieur en IA, informatique, math appliquées) ; doctorat utile pour postes de R&D avancés.",
          "formations_utiles": [
            "Master IA / ML Engineering",
            "Formations spécialisées MLOps et architectures cloud-native",
            "Certifications cloud ML (AWS, GCP, Azure)",
            "Formations avancées en LLMs, GenAI, optimisation de modèles et systèmes de recommandation"
          ],
          "experience_requise": "1–3 ans pour ML Engineer junior, 3–6 ans pour confirmé, 6+ ans pour Staff / Principal ML Engineer.",
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        },
        {
          "id": "deep_learning_engineer",
          "nom": "Deep Learning Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Spécialiste des réseaux de neurones profonds, conçoit des architectures deep learning pour la vision, le langage, le son ou les séries temporelles.",
          "missions_principales": [
            "Concevoir et entraîner des architectures de réseaux de neurones adaptées aux cas d’usage",
            "Optimiser les modèles pour la précision, la robustesse et la performance",
            "Exploiter le GPU et les accélérateurs matériels pour accélérer l’entraînement et l’inférence",
            "Collaborer avec les équipes produit et recherche sur les approches d’IA avancées",
            "Documenter et partager les résultats d’expérimentation"
          ],
          "competences_techniques": [
            "Python avancé, POO, bonnes pratiques de code scientifique",
            "Maîtrise de PyTorch et/ou TensorFlow pour le deep learning",
            "Connaissance des architectures CNN, RNN, LSTM, transformers, diffusion models",
            "Optimisation de l’entraînement (schedule de learning rate, régularisation, augmentation de données)",
            "Utilisation du GPU (CUDA, cuDNN, profiling) et éventuellement TPU",
            "Gestion de larges datasets (chargement, préprocessing, data loaders optimisés)",
            "Notions de recherche (lecture et implémentation d’articles récents)",
            "Suivi d’expériences et reproduction de résultats"
          ],
          "outils_technologies": [
            "PyTorch, TensorFlow, Keras",
            "Hugging Face Transformers, timm, diffusers",
            "CUDA, cuDNN, PyTorch Lightning, Accelerate",
            "Weights & Biases, MLflow, Neptune.ai",
            "Jupyter, VS Code, environnement GPU (on-prem ou cloud)",
            "Outils de profiling GPU/CPU"
          ],
          "competences_soft": [
            "Curiosité scientifique, goût pour l’expérimentation",
            "Capacité à lire et implémenter des papiers de recherche",
            "Rigueur dans l’évaluation et la réplication de résultats"
          ],
          "niveau_etude": "Bac+5 à Bac+8 (Master IA, école d’ingénieur, doctorat en IA/vision/NLP).",
          "formations_utiles": [
            "Master spécialisé en deep learning",
            "Doctorat en IA / vision / NLP",
            "Formations NVIDIA, fast.ai, MOOCs DL avancés"
          ],
          "experience_requise": "1–3 ans en DL appliqué, plus pour les rôles senior/R&D.",
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        },
        {
          "id": "llm_engineer",
          "nom": "LLM Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Conçoit, adapte et optimise des modèles de langage (LLM) pour des produits ou cas d’usage spécifiques, en travaillant sur leur fine-tuning, leur évaluation et leur intégration dans des applications intelligentes.",
          "missions_principales": [
            "Concevoir des systèmes centrés sur les LLM (pipeline d’orchestration, agents intelligents, RAG, tool use)",
            "Adapter ou fine-tuner des modèles pré-entraînés sur des corpus métiers",
            "Évaluer la qualité, la cohérence et la robustesse des réponses LLM (tests, métriques, human eval)",
            "Optimiser prompts, context window, latence et coût d’inférence",
            "Mettre en place des outils d’observabilité, logging et traçabilité (prompt, token, output)",
            "Collaborer avec chercheurs, data engineers et software engineers pour industrialiser les solutions"
          ],
          "competences_techniques": [
            "Excellente maîtrise de Python et manipulation d’APIs LLM (OpenAI, Anthropic, Gemini, Hugging Face, Ollama)",
            "Solides notions de NLP, deep learning et architectures de modèles Transformers",
            "Expérience avec le fine-tuning, l’adaptation par PEFT/LoRA et la génération contrôlée",
            "Pratique des frameworks d’orchestration d’agents et RAG (LangChain, LlamaIndex, CrewAI, LangGraph)",
            "Connaissance des principes d’évaluation et de benchmark (BLEU, RougeL, BERTScore, custom evals)",
            "Utilisation des vector stores et outils de recherche sémantique"
          ],
          "outils_technologies": [
            "LangChain, LlamaIndex, CrewAI, LangGraph, semantic-kernel",
            "Hugging Face, OpenAI, Anthropic, Ollama, vLLM, Unsloth",
            "Vector DB : Pinecone, Qdrant, Weaviate, PGVector",
            "Évaluation : LangFuse, LangSmith, PromptLayer, Traceloop, Weights & Biases",
            "Fine-tuning : PEFT, LoRA, Unsloth, Transformers, PyTorch, AutoTrain",
            "Cloud & déploiement : GCP Vertex AI, AWS Sagemaker, Hugging Face Hub, Docker"
          ],
          "competences_soft": [
            "Rigueur expérimentale et approche scientifique",
            "Curiosité pour les modèles de langage et recherche en NLP",
            "Communication claire avec les équipes produit et IA"
          ],
          "niveau_etude": "Bac+5 (informatique, IA, math appliquées, NLP) ou profil autodidacte très avancé en ingénierie LLM.",
          "formations_utiles": [
            "Formations avancées en NLP et LLM (Hugging Face, DeepLearning.AI, OpenAI)",
            "Cours sur le fine-tuning, prompt engineering et évaluation responsable",
            "Certifications IA appliquée / GenAI des hyperscalers"
          ],
          "experience_requise": "3–6 ans en machine learning, NLP ou développement d’applications basées sur des LLM.",
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        {
          "id": "ai_engineer",
          "nom": "AI Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Construit des systèmes d’IA end-to-end, combinant modèles, règles, APIs, LLMs et intégrations produit pour répondre à des cas d’usage métiers.",
          "missions_principales": [
            "Assembler des composants d’IA (ML classique, deep learning, LLMs, règles, APIs externes) en solutions complètes",
            "Intégrer les systèmes d’IA dans les produits existants (backend, frontend, workflows métier)",
            "Optimiser expérience utilisateur, latence et coûts d’inférence",
            "Assurer la supervision, les logs et la gestion d’erreurs des systèmes IA",
            "Collaborer avec les équipes produit, design et métier pour définir les fonctionnalités IA"
          ],
          "competences_techniques": [
            "Bon niveau en Python et un langage backend (TypeScript/Node, Java, Go selon le contexte)",
            "Connaissance de plusieurs familles de modèles (ML, DL, LLMs, systèmes de recommandation)",
            "Intégration d’APIs d’IA (OpenAI, Anthropic, Vertex AI, Hugging Face Inference)",
            "Patterns d’applications LLM (RAG, agents, outils, memory, évaluation)",
            "Déploiement de services IA (APIs, serverless functions, microservices)",
            "Notions de sécurité applicative (auth, rate limiting, gestion de secrets)"
          ],
          "outils_technologies": [
            "Python, TypeScript/Node.js",
            "Frameworks web : FastAPI, Flask, Express",
            "LLM frameworks : LangChain, LlamaIndex, semantic-kernel, crewAI, langgraph",
            "Fine-tuning : PEFT, LoRA, Unsloth, Transformers, PyTorch, AutoTrain",
            "APIs LLM : OpenAI, Anthropic, Gemini, LLaMA via providers",
            "Vector DB : Pinecone, Weaviate, Qdrant, Redis, PGVector",
            "Cloud : AWS, GCP, Azure (APIs managées d’IA, serverless)",
            "Observabilité : OpenTelemetry, Prometheus, Grafana, langfuse, langsmith, langtrace, datadog"
          ],
          "competences_soft": [
            "Orientation produit et UX",
            "Capacité à prototyper rapidement et itérer",
            "Communication avec designers, PM et métiers"
          ],
          "niveau_etude": "Bac+3 à Bac+5 (informatique/IA), avec forte appétence pour le développement logiciel.",
          "formations_utiles": [
            "Formations en IA appliquée et développement d’applications LLM",
            "Bootcamps IA fullstack",
            "Certifications LLM/GenAI proposées par les hyperscalers"
          ],
          "experience_requise": "2–5 ans en dev logiciel et/ou ML engineering.",
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        {
          "id": "data_platform_engineer",
          "nom": "Data Platform Engineer",
          "categorie": "Ingénierie data & IA",
          "description": "Conçoit, construit et maintient la plateforme data/IA globale (outils, services, standards) utilisée par les équipes data et produit.",
          "missions_principales": [
            "Concevoir l’architecture globale de la plateforme data/IA",
            "Packager des services self-service pour les équipes data (environnements, templates, pipelines)",
            "Gérer l’infrastructure (Kubernetes, cloud, sécurité, monitoring)",
            "Standardiser les outils (suite MLOps, DataOps, gouvernance)",
            "Assurer la scalabilité et l’optimisation des coûts de la plateforme"
          ],
          "competences_techniques": [
            "Architecture systèmes distribués",
            "Maîtrise des principaux composants de la stack data & MLOps",
            "Cloud computing (AWS/GCP/Azure) orienté data et IA",
            "Kubernetes, networking, sécurité, observabilité",
            "Infra as Code (Terraform, Pulumi) et GitOps"
          ],
          "outils_technologies": [
            "Kubernetes, Helm, Istio/Linkerd (selon contexte)",
            "Terraform, Pulumi, ArgoCD",
            "Spark, Kafka, data warehouses cloud",
            "Outils MLOps/DataOps (MLflow, Airflow, dbt, catalogues, quality tools)",
            "Monitoring : Prometheus, Grafana, Loki"
          ],
          "competences_soft": [
            "Leadership technique transversal",
            "Capacité à définir des standards et conventions",
            "Communication avec de multiples équipes",
            "Vision long terme sur l’architecture"
          ],
          "niveau_etude": "Bac+5 (école d’ingénieur / Master informatique, systèmes distribués).",
          "formations_utiles": [
            "Formations avancées en cloud & Kubernetes",
            "Certifications architecte cloud (AWS/GCP/Azure)",
            "Formations data platform / data mesh"
          ],
          "experience_requise": "5+ ans en data engineering/infra, dont expérience significative en architecture.",
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    },
    {
      "metiers": [
        {
          "id": "cdo",
          "nom": "Chief Data Officer (CDO)",
          "categorie": "Gouvernance, qualité, protection",
          "description": "Dirige la stratégie data de l’entreprise. Responsable de la gouvernance, de la valorisation et de la conformité des données pour créer de la valeur business.",
          "missions_principales": [
            "Définir et piloter la stratégie data alignée sur les objectifs business",
            "Superviser la gouvernance des données (qualité, métadonnées, catalogue)",
            "Garantir la conformité RGPD, AI Act et autres réglementations",
            "Construire et animer la plateforme data/IA (outils, standards, budget)",
            "Évangeliser la culture data et mesurer l’impact business des initiatives data",
            "Collaborer avec C-level (CEO, CIO, CTO, CMO) sur la transformation data-driven"
          ],
          "competences_techniques": [
            "Architecture data et modern data stack (lakehouse, data mesh, data product)",
            "Gouvernance data (qualité, catalogue, lineage, ownership)",
            "Conformité RGPD, AI Act, NIST AI RMF, ISO 42001",
            "Cloud data platforms (AWS, GCP, Azure) et coûts optimisation",
            "Data product management et data mesh",
            "AI/ML gouvernance (bias, fairness, explainability, model cards)",
            "KPI data (ROI data projects, data maturity, adoption metrics)",
            "Outils de data governance et catalogue"
          ],
          "outils_technologies": [
            "Data catalogs : Collibra, Alation, DataHub, Amundsen, OpenMetadata",
            "Data governance : Informatica, Talend, Atlan",
            "Data quality : Great Expectations, Soda, Monte Carlo",
            "Cloud data platforms : Snowflake, BigQuery, Databricks",
            "Observabilité data : Monte Carlo, Bigeye",
            "Outils de conformité : Credo AI, Holistic AI",
            "BI executive : Tableau, Power BI, Looker"
          ],
          "competences_soft": [
            "Leadership stratégique et vision business",
            "Capacité à influencer le C-level et à vendre la valeur des données",
            "Gestion du changement et acculturation data",
            "Communication claire et storytelling data",
            "Gestion d’équipe pluridisciplinaire (data scientists, engineers, analysts)"
          ],
          "niveau_etude": "Bac+5 (MBA, école d’ingénieur/commerce, Master data science) + expérience significative.",
          "formations_utiles": [
            "MBA ou executive education (INSEAD, HEC, etc.)",
            "Certifications data governance (CDMP, DAMA)",
            "Formations AI governance et compliance (AI Act, NIST)",
            "Executive programs data leadership (MIT, Stanford)"
          ],
          "experience_requise": "10+ ans en data/analytics, dont 3–5 ans en rôle de gouvernance/leadership data.",
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        },
        {
          "id": "cao",
          "nom": "Chief Analytics Officer (CAO)",
          "categorie": "Gouvernance, qualité, protection",
          "description": "Dirige la stratégie analytique et décisionnelle. Responsable de la création de valeur par l’analytics et l’IA prédictive.",
          "missions_principales": [
            "Définir la roadmap analytics et IA prédictive",
            "Piloter les projets d’analytics avancés et ML",
            "Mesurer l’impact business des insights et modèles",
            "Construire et animer les équipes analytics/data science",
            "Aligner les priorités analytics avec les objectifs stratégiques"
          ],
          "competences_techniques": [
            "Analytics avancé et machine learning",
            "Gestion de portefeuille de projets data science",
            "KPI analytics (ROI, précision modèles, adoption)",
            "Roadmapping technique et priorisation",
            "Cloud analytics (Databricks, Snowflake ML, BigQuery ML)"
          ],
          "outils_technologies": [
            "Cloud analytics platforms : Databricks, Snowflake ML, BigQuery ML",
            "BI avancé : Looker, Tableau, Power BI",
            "MLOps : MLflow, Vertex AI, SageMaker",
            "Outils de gestion de projets data (Jira, Asana, Monday)"
          ],
          "competences_soft": [
            "Leadership technique et business",
            "Storytelling analytics",
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        {
          "id": "data_manager",
          "nom": "Data Manager",
          "categorie": "Gouvernance, qualité, protection",
          "description": "Gère les ressources et processus data. Assure la qualité, la disponibilité et la conformité des données au quotidien.",
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            "Coordonner les équipes data sur les projets transversaux",
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            "Conformité RGPD de base",
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            "Outils de data governance"
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            "Data quality : Great Expectations, Soda Core",
            "Excel/Google Sheets avancé",
            "SQL, BI tools pour audits"
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          "competences_soft": [
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          "niveau_etude": "Bac+3 à Bac+5 (informatique, gestion, commerce).",
          "formations_utiles": [
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        {
          "id": "data_product_manager",
          "nom": "Data Product Manager",
          "categorie": "Produits, projets, stratégie",
          "description": "Gère les produits data comme un produit business (data lake, data mart, API data, ML features). Définit la roadmap et la valeur métier.",
          "missions_principales": [
            "Définir la vision et roadmap du produit data",
            "Prioriser les features selon l’impact business et les besoins des consommateurs data",
            "Collaborer avec data engineers, analysts et métiers pour définir les spécifications",
            "Mesurer l’adoption, l’usage et le ROI du produit data",
            "Évangeliser le produit auprès des consommateurs internes"
          ],
          "competences_technences": [
            "Data product thinking (usage, adoption, métriques produit)",
            "Compréhension technique de la stack data (warehouse, pipelines, quality)",
            "Roadmapping et priorisation (RICE, Kano, etc.)",
            "KPI data products (usage metrics, data freshness, adoption)",
            "Data mesh et data productisation",
            "SQL et BI pour valider les hypothèses"
          ],
          "outils_technologies": [
            "Roadmapping : Productboard, Aha!, Jira, Notion",
            "Prototyping : Figma pour mockups de dashboards/API",
            "Analytics : Mixpanel, Amplitude pour adoption data",
            "BI : Looker, Tableau, Power BI",
            "SQL, dbt pour valider les datasets",
            "Data catalogs pour discovery"
          ],
          "competences_soft": [
            "Product mindset appliqué aux données",
            "Communication avec métiers et data teams",
            "Storytelling data product",
            "Gestion de stakeholders multiples"
          ],
          "niveau_etude": "Bac+5 (école commerce, Master data, école d’ingénieur).",
          "formations_utiles": [
            "Formations product management",
            "Formations data product (Reforge, Product School)",
            "Certifications product management"
          ],
          "experience_requise": "3+ ans product management ou data analytics.",
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        },
        {
          "id": "ai_product_manager",
          "nom": "AI Product Manager",
          "categorie": "Produits, projets, stratégie",
          "description": "Gère les produits utilisant l’IA (chatbots, systèmes de recommandation, agents IA, outils GenAI). Aligne besoins métier et capacités techniques IA.",
          "missions_principales": [
            "Définir la roadmap produit IA",
            "Prioriser features IA selon ROI, UX et faisabilité technique",
            "Collaborer avec data scientists, ML engineers et design",
            "Tester et mesurer l’efficacité des modèles IA (A/B testing, métriques produit)",
            "Gérer les risques éthiques et réglementaires de l’IA"
          ],
          "competences_techniques": [
            "Compréhension des modèles ML/LLM (capacités, limites, coûts)",
            "Patterns d’applications IA (RAG, agents, outils, fine-tuning)",
            "Métriques produit IA (précision, latence, coût d’inférence, satisfaction user)",
            "UX d’interactions IA (prompts, feedback loops)",
            "Notions d’éthique IA et compliance AI Act"
          ],
          "outils_technologies": [
            "Productboard, Jira, Aha! pour roadmapping",
            "Figma pour mockups d’interfaces IA",
            "Amplitude, Mixpanel pour analytics IA",
            "APIs LLM : OpenAI, Anthropic, Vertex AI",
            "LangChain, LlamaIndex pour prototyping",
            "Prompt engineering tools"
          ],
          "competences_soft": [
            "Product thinking appliqué à l’IA",
            "Collaboration avec data scientists et ML engineers",
            "Gestion des attentes sur les capacités IA",
            "Sensibilité éthique et UX"
          ],
          "niveau_etude": "Bac+5 (école commerce, Master IA/data, école d’ingénieur).",
          "formations_utiles": [
            "Formations product management IA",
            "Bootcamps GenAI product",
            "Certifications LLM/GenAI"
          ],
          "experience_requise": "2–4 ans product management + appétence IA.",
          "embedding": [
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        },
        {
          "id": "consultant_data",
          "nom": "Consultant Data",
          "categorie": "Produits, projets, stratégie",
          "description": "Conseille les entreprises sur leur transformation data. Diagnostique, propose des architectures et accompagne la mise en œuvre.",
          "missions_principales": [
            "Diagnostiquer la maturité data de l’entreprise",
            "Proposer des architectures data adaptées",
            "Accompagner la mise en œuvre des projets data",
            "Former les équipes aux bonnes pratiques",
            "Mesurer l’impact des transformations data"
          ],
          "competences_techniques": [
            "Data maturity assessment",
            "Architecture data (lakehouse, data mesh, data fabric)",
            "Modern data stack",
            "Cloud migration data",
            "Data governance frameworks",
            "ROI calculation pour projets data"
          ],
          "outils_technologies": [
            "Outils d’audit data (Collibra, Talend, Informatica)",
            "Cloud platforms (AWS, GCP, Azure)",
            "Modern data stack (dbt, Airflow, Snowflake, etc.)",
            "Outils de modélisation (ER/Studio, Lucidchart)",
            "Excel, Power BI pour business cases"
          ],
          "competences_soft": [
            "Consulting mindset",
            "Communication claire et structurée",
            "Gestion de projet et stakeholders",
            "Pédagogie et formation"
          ],
          "niveau_etude": "Bac+5 (école d’ingénieur, commerce, Master data).",
          "formations_utiles": [
            "Formations consulting data",
            "Certifications cloud et data engineering",
            "MBA ou mastère spécialisé"
          ],
          "experience_requise": "3–7 ans en data/projects.",
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        },
        {
          "id": "data_project_manager",
          "nom": "Chef de Projet Data",
          "categorie": "Produits, projets, stratégie",
          "description": "Pilote les projets data de bout en bout. Gère les ressources, risques, budget et livraison des initiatives data.",
          "missions_principales": [
            "Planifier et piloter les projets data (scope, planning, budget)",
            "Gérer les équipes pluridisciplinaires (data engineers, scientists, analysts)",
            "Suivre les risques et les indicateurs de projet",
            "Communiquer avec les sponsors et stakeholders",
            "Assurer la livraison conforme aux attentes"
          ],
          "competences_techniques": [
            "Gestion de projet agile (Scrum, Kanban)",
            "Compréhension technique data/IA",
            "KPI projet data (data freshness, modèle accuracy, adoption)",
            "Outils de gestion de projet",
            "Notions de budget cloud data"
          ],
          "outils_technologies": [
            "Jira, Confluence, Asana, Monday.com",
            "MS Project, Smartsheet",
            "Power BI/Tableau pour dashboards projet",
            "GitHub/GitLab pour suivi technique"
          ],
          "competences_soft": [
            "Leadership d’équipe",
            "Gestion de stakeholders",
            "Résolution de conflits",
            "Communication claire"
          ],
          "niveau_etude": "Bac+5 (management, informatique).",
          "formations_utiles": [
            "Certifications PMP, Scrum Master, Prince2",
            "Formations gestion de projet data"
          ],
          "experience_requise": "3–7 ans gestion de projet IT/data.",
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        },
        {
          "id": "data_strategist",
          "nom": "Data Strategist",
          "categorie": "Produits, projets, stratégie",
          "description": "Définit la stratégie data de l’entreprise. Identifie les opportunités data et aligne avec les objectifs business.",
          "missions_principales": [
            "Identifier les opportunités data stratégiques",
            "Construire la roadmap data à 2–3 ans",
            "Mesurer l’impact business des initiatives data",
            "Conseiller le C-level sur les investissements data",
            "Veille technologique et benchmarking data"
          ],
          "competences_techniques": [
            "Data strategy frameworks",
            "Data maturity models",
            "ROI calculation data projects",
            "Benchmarking data stack",
            "Trends data (data mesh, GenAI, real-time)"
          ],
          "outils_technologies": [
            "Excel, Google Sheets pour business cases",
            "Power BI/Tableau pour présentations",
            "Outils de veille (Gartner, Forrester)",
            "Notion/Confluence pour documentation"
          ],
          "competences_soft": [
            "Vision stratégique",
            "Communication C-level",
            "Storytelling business",
            "Benchmarking"
          ],
          "niveau_etude": "Bac+5 (MBA, école commerce, Master data).",
          "formations_utiles": [
            "MBA, executive education",
            "Formations data strategy"
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          "experience_requise": "7+ ans data, 3+ ans stratégie.",
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        {
          "id": "data_steward",
          "nom": "Data Steward",
          "categorie": "Gouvernance, qualité, protection",
          "description": "Assure la qualité et la gouvernance des données au quotidien. Documente, contrôle et corrige les données.",
          "missions_principales": [
            "Contrôler la qualité des données selon les règles métier",
            "Documenter les métadonnées et glossaires",
            "Corriger les anomalies et erreurs détectées",
            "Former les contributeurs aux bonnes pratiques data",
            "Participer aux audits qualité"
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          "competences_techniques": [
            "Data quality rules et métriques",
            "Métadonnées et glossaires",
            "SQL pour contrôles qualité",
            "Excel/Google Sheets avancé",
            "Outils data quality"
          ],
          "outils_technologies": [
            "Excel, Google Sheets",
            "SQL",
            "Data quality tools (Great Expectations, Soda)",
            "Data catalogs (DataHub, Amundsen)",
            "Jira pour tracking des issues data"
          ],
          "competences_soft": [
            "Rigueur et attention aux détails",
            "Collaboration avec métiers et IT",
            "Pédagogie",
            "Organisation"
          ],
          "niveau_etude": "Bac+2 à Bac+3.",
          "formations_utiles": [
            "Formations data quality/governance",
            "Certifications data stewardship"
          ],
          "experience_requise": "1–3 ans data/analytique.",
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    {
      "metiers": [
        {
          "id": "nlp_engineer",
          "nom": "NLP Engineer / Ingénieur TAL",
          "categorie": "Spécialistes techniques IA",
          "description": "Développe des solutions de traitement automatique du langage naturel (chatbots, analyse de sentiments, NER, traduction, résumé).",
          "missions_principales": [
            "Construire des pipelines NLP end-to-end (préprocessing, embedding, modèles, postprocessing)",
            "Fine-tuner des modèles de langage pour des cas d’usage spécifiques",
            "Implémenter des patterns RAG et agents conversationnels",
            "Optimiser la performance et la latence des systèmes NLP",
            "Évaluer la qualité des modèles NLP (BLEU, ROUGE, perplexity, human eval)"
          ],
          "competences_techniques": [
            "Maîtrise des modèles transformers et LLMs",
            "Fine-tuning et PEFT (LoRA, QLoRA)",
            "RAG (retrieval-augmented generation) et vector search",
            "NLP evaluation metrics (BLEU, ROUGE, BERTScore, perplexity)",
            "Embeddings et vector databases",
            "Prompt engineering et chain-of-thought",
            "Langage spécifique (NER, POS tagging, dependency parsing)"
          ],
          "outils_technologies": [
            "Hugging Face Transformers, PEFT",
            "spaCy, NLTK, Stanford CoreNLP",
            "LangChain, LlamaIndex, Haystack",
            "Vector DB : Pinecone, Weaviate, Qdrant, PGVector",
            "APIs : OpenAI GPT, Anthropic Claude, Google Gemini, Cohere",
            "FastAPI pour APIs NLP",
            "Docker/Kubernetes pour déploiement"
          ],
          "competences_soft": [
            "Compréhension linguistique",
            "Curiosité pour les langues et cultures",
            "Collaboration avec UX et métiers"
          ],
          "niveau_etude": "Bac+5 (Master IA/NLP, école d’ingénieur).",
          "formations_utiles": [
            "Master spécialisation NLP",
            "Formations Hugging Face, spaCy",
            "MOOCs NLP avancés"
          ],
          "experience_requise": "1–3 ans NLP/ML.",
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        },
        {
          "id": "cv_engineer",
          "nom": "Computer Vision Engineer",
          "categorie": "Spécialistes techniques IA",
          "description": "Développe des solutions d’analyse d’images et vidéos (détection d’objets, segmentation, tracking, 3D reconstruction).",
          "missions_principales": [
            "Construire des pipelines vision end-to-end",
            "Fine-tuner des modèles de vision (YOLO, DETR, Segment Anything)",
            "Optimiser pour edge devices et temps réel",
            "Implémenter tracking et multi-object tracking",
            "Évaluer la précision (mAP, IoU, FPS)"
          ],
          "competences_techniques": [
            "CNN et architectures vision (ResNet, EfficientNet, Vision Transformers)",
            "Object detection (YOLOv8, YOLOv9, RT-DETR)",
            "Segmentation (Mask R-CNN, SAM, U-Net)",
            "Tracking (DeepSORT, ByteTrack)",
            "Optimisation edge (TensorRT, ONNX Runtime, TFLite)",
            "3D vision de base (depth estimation, point clouds)"
          ],
          "outils_technologies": [
            "PyTorch, TensorFlow, OpenCV",
            "Ultralytics YOLO, Detectron2, MMdetection",
            "Segment Anything Model (SAM)",
            "ONNX, TensorRT, OpenVINO",
            "Docker pour déploiement edge",
            "Roboflow pour datasets et annotation"
          ],
          "competences_soft": [
            "Sens visuel",
            "Rigueur dans l’évaluation",
            "Collaboration avec hardware"
          ],
          "niveau_etude": "Bac+5 (Master vision/IA).",
          "formations_utiles": [
            "Formations CV avancées",
            "Certifications NVIDIA"
          ],
          "experience_requise": "1–3 ans CV/ML.",
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        },
        {
          "id": "llm_engineer",
          "nom": "LLM Engineer",
          "categorie": "Spécialistes techniques IA",
          "description": "Spécialiste des Large Language Models. Fine-tune, déploie et optimise des modèles de langage pour des cas d’usage spécifiques.",
          "missions_principales": [
            "Fine-tuner des LLMs open-source (Llama, Mistral, Mixtral)",
            "Implémenter RAG, agents, tools et memory",
            "Optimiser coûts et latence (quantization, distillation)",
            "Évaluer qualité (human eval, LLM-as-judge)",
            "Déployer en production (APIs, streaming, edge)"
          ],
          "competences_techniques": [
            "Fine-tuning (PEFT, LoRA, QLoRA)",
            "RAG (advanced retrieval, reranking, hybrid search)",
            "Agents LLM (tools calling, reasoning)",
            "Quantization (bitsandbytes, GPTQ, AWQ)",
            "Evaluation LLM (human eval, LLM-as-judge, benchmarks)",
            "Prompt engineering avancé"
          ],
          "outils_technologies": [
            "Hugging Face (Transformers, TRL, PEFT)",
            "Unsloth, Axolotl pour fine-tuning rapide",
            "LangChain, LlamaIndex, Haystack",
            "vLLM, Text Generation Inference pour serving",
            "Vector DB : Pinecone, Weaviate, Milvus",
            "Ollama, LM Studio pour local eval"
          ],
          "competences_soft": [
            "Rapidité d’apprentissage",
            "Expérimentation",
            "Collaboration"
          ],
          "niveau_etude": "Bac+5 (Master IA/NLP).",
          "formations_utiles": [
            "Formations Hugging Face LLM",
            "Bootcamps LLM engineering"
          ],
          "experience_requise": "1–2 ans ML/NLP.",
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        },
        {
          "id": "prompt_engineer",
          "nom": "Prompt Engineer",
          "categorie": "Spécialistes techniques IA",
          "description": "Optimise les prompts et interactions avec les LLMs pour maximiser la qualité et la cohérence des réponses.",
          "missions_principales": [
            "Concevoir des prompts optimisés pour différents LLMs",
            "Tester et itérer sur les prompts (A/B testing)",
            "Créer des templates et patterns réutilisables",
            "Former les équipes aux bonnes pratiques de prompting",
            "Mesurer l’efficacité des prompts (qualité, coût, latence)"
          ],
          "competences_techniques": [
            "Prompt engineering techniques (chain-of-thought, few-shot, tree-of-thoughts)",
            "Compréhension des capacités/limites des différents LLMs",
            "A/B testing de prompts",
            "Métriques d’évaluation (human eval, LLM-as-judge)"
          ],
          "outils_technologies": [
            "Promptfoo, LangSmith, PromptLayer",
            "OpenAI Playground, Anthropic Console",
            "LangChain, LlamaIndex pour templating",
            "Jupyter pour experimentation"
          ],
          "competences_soft": [
            "Créativité linguistique",
            "Sens analytique",
            "Pédagogie"
          ],
          "niveau_etude": "Bac+3 minimum.",
          "formations_utiles": [
            "Bootcamps prompt engineering",
            "Cours OpenAI/Anthropic"
          ],
          "experience_requise": "0–1 an.",
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        },
        {
          "id": "data_viz_specialist",
          "nom": "Data Visualisation Specialist",
          "categorie": "Visualisation, décisionnel, métier",
          "description": "Crée des visualisations impactantes et narratives pour communiquer les insights data de manière accessible et persuasive.",
          "missions_principales": [
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            "Créer des dashboards interactifs et storytelling data",
            "Optimiser l’UX et l’accessibilité des visualisations",
            "Collaborer avec data analysts et métiers",
            "Produire des présentations data impactantes"
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            "Principes de data visualization (Tufte, Cleveland)",
            "Design graphique et UX/UI pour data",
            "Utilisation avancée des outils de visualisation",
            "SQL pour préparation des datasets viz",
            "Animation et interactivité data"
          ],
          "outils_technologies": [
            "Tableau, Power BI, Looker",
            "D3.js, Observable, Vega-Lite",
            "Figma pour design",
            "Flourish, Datawrapper pour storytelling",
            "ggplot2, Plotly pour Python/R"
          ],
          "competences_soft": [
            "Sens esthétique et créativité",
            "Storytelling data",
            "Compréhension du public cible"
          ],
          "niveau_etude": "Bac+3 (design, informatique).",
          "formations_utiles": [
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            "Certifications Tableau/Power BI"
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        {
          "id": "data_architect",
          "nom": "Data Architect",
          "categorie": "Infrastructure et sécurité",
          "description": "Conçoit l’architecture globale des systèmes de données (data lake, warehouse, pipelines, gouvernance) pour répondre aux besoins actuels et futurs.",
          "missions_principales": [
            "Définir l’architecture data stratégique",
            "Choisir les technologies adaptées (lakehouse vs warehouse, on-prem vs cloud)",
            "Modéliser les données (data modeling, schemas)",
            "Garantir scalabilité, performance, coûts",
            "Documenter l’architecture et standards"
          ],
          "competences_techniques": [
            "Architecture lakehouse/data mesh/data fabric",
            "Modélisation de données (étoile, data vault, Kimball)",
            "Benchmarking technologies data",
            "Optimisation coûts/performance cloud",
            "Data governance architecture"
          ],
          "outils_technologies": [
            "Snowflake, Databricks, BigQuery",
            "Terraform, Lucidchart, dbt",
            "Collibra, Alation pour gouvernance"
          ],
          "competences_soft": [
            "Vision stratégique",
            "Communication technique/business",
            "Benchmarking"
          ],
          "niveau_etude": "Bac+5 (école d’ingénieur).",
          "formations_utiles": [
            "Certifications architecte cloud",
            "Formations data modeling"
          ],
          "experience_requise": "5+ ans data engineering.",
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        {
          "id": "ai_ethics_specialist",
          "nom": "AI Ethics Specialist",
          "categorie": "Formation, accompagnement, éthique",
          "description": "Assure l’éthique et la conformité des systèmes IA. Évalue les risques (bias, fairness, privacy) et propose des solutions responsables.",
          "missions_principales": [
            "Auditer les modèles IA (bias, fairness, robustness)",
            "Implémenter des garde-fous et mitigations",
            "Rédiger model cards et documentation éthique",
            "Former aux bonnes pratiques IA responsable",
            "Conseiller sur AI Act et conformité"
          ],
          "competences_techniques": [
            "Bias detection et mitigation",
            "Fairness metrics (demographic parity, equal opportunity)",
            "Explainability (SHAP, LIME, counterfactuals)",
            "AI Act classification et obligations",
            "Privacy (DP-SGD, federated learning)"
          ],
          "outils_technologies": [
            "IBM AI Fairness 360, Aequitas",
            "SHAP, LIME pour explainability",
            "Credo AI, Holistic AI pour gouvernance",
            "What-If Tool (Google), Facets",
            "AI FactSheets 360"
          ],
          "competences_soft": [
            "Pensée critique éthique",
            "Communication interdisciplinaire",
            "Pédagogie"
          ],
          "niveau_etude": "Bac+5 (éthique, droit, informatique).",
          "formations_utiles": [
            "Certifications AI ethics/governance",
            "Formations AI Act"
          ],
          "experience_requise": "2–5 ans IA/compliance.",
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        },
        {
          "id": "dpo",
          "nom": "Data Protection Officer (DPO)",
          "categorie": "Gouvernance, qualité, protection",
          "description": "Garantit la conformité RGPD et protection des données personnelles dans les projets data/IA.",
          "missions_principales": [
            "Effectuer les DPIA (Data Protection Impact Assessment)",
            "Gérer les demandes d’exercice des droits (droit à l’oubli, etc.)",
            "Conseiller sur la conformité des traitements",
            "Auditer les systèmes de données",
            "Former aux obligations RGPD"
          ],
          "competences_techniques": [
            "RGPD, CNIL, ePrivacy",
            "Data mapping et register of processing",
            "DPIA, transferts internationaux",
            "Sécurité des données (encryption, pseudonymisation)"
          ],
          "outils_technologies": [
            "Outils compliance (OneTrust, TrustArc)",
            "Excel pour data mapping",
            "Outils de chiffrement"
          ],
          "competences_soft": [
            "Rigueur juridique",
            "Communication",
            "Pédagogie"
          ],
          "niveau_etude": "Bac+5 (droit, gestion).",
          "formations_utiles": [
            "Certifications DPO/RGPD"
          ],
          "experience_requise": "3+ ans compliance.",
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