data/README.md

Redrob x Hack2Skill - India Runs : Talentry AI submission

This folder contains the deliverable shortlist for the Redrob x Hack2Skill - India Runs Data & AI Challenge.

Hackathon problem statement (verbatim)

Build an Intelligent Candidate Discovery & Ranking Engine.

Given a pool of 100,000 anonymised candidates (candidates.jsonl, schema in candidate_schema.json) and a single Job Description for a Senior AI Engineer role at Redrob, produce a ranked top 100 shortlist as a CSV / XLSX matching sample_submission.csv and conforming to the rules in validate_submission.py.

Constraints:

• CPU only, no GPU.
• No network / LLM calls during ranking.
• Reproducible: a single command must regenerate the submission.
• Submission must pass the official validate_submission.py validator.
• Evaluation is automated (LightGBM ground-truth match) and human (manual reasoning review at Stage 4).

What is in this folder

data/
├── README.md                # this file
├── raw/
│   ├── candidates.jsonl     # full 100,000-candidate pool (git-ignored)
│   └── sample_candidates.json   # 50-row fixture used by the HF Space
├── output/                  # local CLI smoke runs land here (git-ignored)
└── redrob_submission/
    ├── submission.csv       # *** OFFICIAL DELIVERABLE ***
    └── submission.xlsx      # same shortlist, styled for human review

Inputs used to produce redrob_submission/

Item	Value
Candidates file	data/raw/candidates.jsonl
Candidates count	100,000 records
Job Description	India_runs_data_and_ai_challenge/job_description.docx
JD role / seniority	Senior AI Engineer (Founding Team), 5-9 yrs
Top-K	100
Engine version	talentry-ai v1.0.0 (commit f4ea7a8+)

Reproduce in one command

cd talentry-ai
source .venv/bin/activate            # or: pip install -e ".[dev]"

python -m talentry.cli.rank \
    --candidates data/raw/candidates.jsonl \
    --jd "/path/to/job_description.docx" \
    --out data/redrob_submission/submission.csv \
    --also-xlsx

Then validate against the official checker:

python "../[PUB] India_runs_data_and_ai_challenge/India_runs_data_and_ai_challenge/validate_submission.py" \
       data/redrob_submission/submission.csv
# -> Submission is valid.

Output summary

• submission.csv - 1 header row + 100 ranked candidates, validator-clean (candidate_id,rank,score,reasoning).
• submission.xlsx - same shortlist materialised through openpyxl with frozen header row, sized columns and wrapped reasoning column. Both files have identical ranking and reasoning - use whichever your workflow prefers.

Top 5 candidates produced for this JD

rank	candidate_id	score	one-line summary
1	CAND_0086022	1.0673	Senior Applied Scientist, 5.3 yrs, Kolkata - retrieval/ranking work at Sarvam AI
2	CAND_0068351	1.0476	Lead AI Engineer, 6.4 yrs, Delhi - retrieval/ranking work at Sarvam AI
3	CAND_0002025	1.0272	Senior AI Engineer, 5.9 yrs, Trivandrum - retrieval/ranking work at Apple
4	CAND_0008425	1.0240	Senior NLP Engineer, 7.8 yrs, Kolkata - retrieval/ranking work at Ola
5	CAND_0018499	1.0181	Senior ML Engineer, 7.2 yrs, Noida - retrieval/ranking work at Zomato

Run the CLI again at any time to refresh the table; the ranker is fully deterministic given the same candidates.jsonl + JD pair.

How the engine ranks (summary)

1. Stream-ingest the 100K JSONL pool through a slotted-dataclass loader.
2. Schema-validate every record against candidate_schema.json (failures surface as a git-diff-style report in the UI).
3. Parse the JD into a JobRequirements DTO (role family, seniority band, must / nice / disqualifier skills).
4. Score every candidate on 6 explainable signals: title-career alignment (anti keyword-stuffer), hybrid BM25 + TF-IDF semantic JD fit, skill-evidence with endorsement trust, experience-band match, location & logistics, behavioural availability.
5. Honeypot guard down-ranks impossible profiles (e.g. 8 yrs at a 3-year-old company).
6. Compose a 1-2 sentence reasoning citing real facts from the profile.
7. Write the validator-clean CSV + XLSX.

Runtime on a single CPU for the full 100K pool: ~3 min 23 sec wall-clock.

License

MIT - see ../LICENSE.