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"reasoning": "Good alignment -- production ML track record at Sarvam AI: the work described goes beyond experimentation into live systems with real-user stakes -- a competitive signal for this role's primary bar." }, { "rank": 87, "score": 0.380854, "candidate_id": "CAND_0034177", "name": "Shaurya Sen", "current_title": "Senior Software Engineer (ML)", "current_company": "Mad Street Den", "location": "Ahmedabad, Gujarat, India", "years_of_experience": 3.6, "derived_experience_years": 3.5833333333333335, "education_summary": "M.Tech from IIT Delhi", "components": { "skill_match": { "raw": 0.0760768788870572, "weight": 0.25, "weighted": 0.0190192197217643 }, "semantic_fit": { "raw": 0.5686942338943481, "weight": 0.3, "weighted": 0.17060827016830443 }, "career_fit": { "raw": 0.5922675616071357, "weight": 0.2, "weighted": 0.11845351232142715 }, "experience_fit": { "raw": 0.5462202448855427, "weight": 0.08, "weighted": 0.043697619590843416 }, "education_fit": { "raw": 0.9666666666666667, 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SOFT:title_chaser_sub_18m_hops]" ], "reasoning": "Passable fit -- Krutrim shows up as a context where ML went to production, not just to a notebook -- the description puts this in a sound tier for what the role is actually asking for. Even so, tenure stability is the soft spot here: 67% of roles under 18 months, averaging 25 months apiece, which tempers the otherwise positive read of this profile." }, { "rank": 89, "score": 0.380019, "candidate_id": "CAND_0042029", "name": "Dhruv Joshi", "current_title": "Senior Data Scientist", "current_company": "Flipkart", "location": "Delhi, Delhi, India", "years_of_experience": 6.5, "derived_experience_years": 6.5, "education_summary": "B.E. from PES University", "components": { "skill_match": { "raw": 0.17361422015642422, "weight": 0.25, "weighted": 0.043403555039106055 }, "semantic_fit": { "raw": 0.5475103855133057, "weight": 0.3, "weighted": 0.1642531156539917 }, "career_fit": { "raw": 0.81016049193288, "weight": 0.2, "weighted": 0.162032098386576 }, "experience_fit": { "raw": 0.6351945453588164, "weight": 0.08, "weighted": 0.05081556362870531 }, "education_fit": { "raw": 0.8833333333333333, "weight": 0.04, "weighted": 0.035333333333333335 }, "credibility": { "raw": 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match -- evaluation framework usage in evidence at Flipkart: the role description references ranking metrics (NDCG, MRR, or similar), which is the rigour signal the JD is hiring for." }, { "rank": 90, "score": 0.379223, "candidate_id": "CAND_0017648", "name": "Arnav Mishra", "current_title": "AI Specialist", "current_company": "Freshworks", "location": "Mumbai, Maharashtra, India", "years_of_experience": 4.0, "derived_experience_years": 3.9166666666666665, "education_summary": "M.E. from NIT Surathkal", "components": { "skill_match": { "raw": 0.13888888888888887, "weight": 0.25, "weighted": 0.03472222222222222 }, "semantic_fit": { "raw": 0.5804136991500854, "weight": 0.3, "weighted": 0.17412410974502562 }, "career_fit": { "raw": 0.8258044907773208, "weight": 0.2, "weighted": 0.16516089815546417 }, "experience_fit": { "raw": 0.5576659822039699, "weight": 0.08, "weighted": 0.04461327857631759 }, "education_fit": { "raw": 1.0, "weight": 0.04, "weighted": 0.04 }, "credibility": { "raw": 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Worth flagging: a 50% hop rate is a pattern that calls for a direct retention conversation, rather than an assumption that the next stop will be a long one." }, { "rank": 100, "score": 0.376483, "candidate_id": "CAND_0050876", "name": "Vivaan Shah", "current_title": "Applied ML Engineer", "current_company": "Freshworks", "location": "Kolkata, West Bengal, India", "years_of_experience": 6.0, "derived_experience_years": 5.916666666666667, "education_summary": "M.S. from Stanford University", "components": { "skill_match": { "raw": 0.1924806164246883, "weight": 0.25, "weighted": 0.048120154106172076 }, "semantic_fit": { "raw": 0.5918897986412048, "weight": 0.3, "weighted": 0.17756693959236144 }, "career_fit": { "raw": 0.9151550907009776, "weight": 0.2, "weighted": 0.1830310181401955 }, "experience_fit": { "raw": 0.6193817020305726, "weight": 0.08, "weighted": 0.04955053616244581 }, "education_fit": { "raw": 1.0, "weight": 0.04, "weighted": 0.04 }, "credibility": { "raw": 0.07998661311914324, "weight": 0.1, "weighted": 0.007998661311914323 }, "archetype_fit": { "raw": 0.5226795673370361, "weight": 0.03, "weighted": 0.015680387020111083 } }, "competency": { "groups": { "retr": { "trust": 0.0, "in_career": 0.0, "semantic": 0.0, "competency": 0.0, "matched_tokens": [] }, "rank": { "trust": 0.0, "in_career": 0.5, "semantic": 0.0, "competency": 0.16666666666666666, "matched_tokens": [ "ranking", "relevance" ] }, "recsys": { "trust": 0.0, "in_career": 0.25, "semantic": 0.0, "competency": 0.08333333333333333, "matched_tokens": [ "recommendation" ] }, "ir": { "trust": 0.8360056412791609, "in_career": 0.25, "semantic": 0.0, "competency": 0.2996640714962872, "matched_tokens": [ "embeddings", "faiss" ] }, "nlp": { "trust": 0.0, "in_career": 0.3333333333333333, "semantic": 0.0, "competency": 0.1111111111111111, "matched_tokens": [ "transformer" ] }, "llm": { "trust": 0.7, "in_career": 0.0, "semantic": 0.0, "competency": 0.07632838762997012, "matched_tokens": [ "prompt" ] }, "mle": { "trust": 0.8073155104667413, "in_career": 0.0, "semantic": 0.0, "competency": 0.0, "matched_tokens": [ "pytorch", "scikit" ] }, "mlops": { "trust": 0.8836081043522618, "in_career": 0.0, "semantic": 0.0, "competency": 0.026866946237116385, "matched_tokens": [ "kubeflow", "mlops" ] }, "eval": { "trust": 0.0, "in_career": 0.0, "semantic": 0.0, "competency": 0.0, "matched_tokens": [] } }, "top3_groups": [ "ir", "rank", "nlp" ], "skill_match": 0.1924806164246883, "dabbler_groups": [] }, "eligibility": { "is_eligible": true, "hard_blocks": [], "soft_penalties": [ "notice_over_30" ] }, "integrity_gate_passed": true, "behavioral_multiplier": 0.9512380952380952, "logistics_multiplier": 0.5, "logistics": { "location_fit": "india_non_relocatable", "notice_fit": "over_30_higher_bar", "notice_period_days": 90 }, "behavioral": { "availability_status": "present", "responsiveness_status": "present", "response_rate": 0.67, "github_activity_score": 86.1, "expected_salary_min_max": [ 27.8, 49.5 ] }, "risk_badges": [ "[⚠️ SOFT:notice_over_30]" ], "reasoning": "Reasonable case -- hybrid retrieval technology visible in the skills: FAISS -- a strong alignment with the JD's hybrid-retrieval requirement. On the downside, notice period (90 days, 'over 30 higher bar') is the practical catch here -- everything upstream of the offer stage looks clean." } ]