ayushpahuja/redrob-candidate-ranker

Redrob Intelligent Candidate Discovery

Project Overview

This repository generates a ranked CSV of the best 100 candidates from a massive pool of 100,000 candidates. Built specifically for the Redrob Intelligent Candidate Discovery Hackathon, it evaluates engineering profiles against a core Applied ML / Search matching rubric.

This is a strictly offline, high-performance, deterministic ranking engine that processes all candidates and exports the results along with organic, dynamically-generated 1-2 sentence evidence-based reasoning strings.

Architecture

The system uses a single-pass feature extraction and scoring pipeline to process candidates offline without relying on external network calls or non-deterministic APIs.

1. Raw Candidates (100k) -> Loaded into memory.
2. Feature Extraction (features.py) -> Extracts exact text matches, phrase hits, and behavioral flags.
3. Scoring & Rules (scorer.py) -> Evaluates eligibility, assigns titles to classes, and scores depth of skills.
4. Reasoning Generation -> Constructs evidence-grounded justification strings using a composable template engine.
5. Sort & Slice -> Filters invalid profiles, sorts by score descending, and slices the top 100.
6. Output -> Writes submission.csv.

Feature Engineering

The system evaluates candidates using a heavily engineered, multi-layered rubric rather than naive keyword matching:

• Title Match & Class (Primary Gate): Determines base eligibility (strong_ml, adjacent_eng, excluded_domain, off_domain).
• Shipped System Evidence: Looks for phrases indicating actual production work (e.g., "deployed", "scaled") combined with technical domains, rather than just raw tags.
• Family Scores (Skill Depth): Measures experience depth across specific tech families (e.g., Vector DBs, Hybrid Search, ML Infrastructure).
• Trajectory & Seniority: Analyzes progression to ensure seniority is non-decreasing and converging toward applied ML.
• Behavioral Signals: Penalizes extended platform inactivity or poor recruiter response rates.

Ranking Pipeline

• Sorting Mechanism: Candidates are sorted primarily by their aggregate final_score.
• Tie-Breaking: If scores match exactly, a secondary tie-breaker uses internal feature depth (e.g., shipped systems, convergence) to ensure a stable sort.

Honeypot Detection

The competition explicitly includes honeypot candidates. We catch them via consistency_flags and disqualifier_flags:

• Seniority Inversions: Flagging profiles that show erratic jumps (e.g., VP of Engineering stepping down to Junior).
• Timeline Contradictions: Detecting impossibly dense parallel roles or years of experience that exceed biological possibility.
• Domain Mismatch: Catching profiles claiming heavy ML experience but whose titles firmly root them in an explicitly excluded domain.

Keyword Stuffing Detection

Vector embeddings (e.g., cosine similarity) are easily fooled by keyword stuffers who copy/paste the JD into their profiles. Our feature engine defends against this:

• We explicitly look for corroborated evidence: mentions of the skill alongside verbs that indicate production deployment (e.g., "scaled PyTorch training pipeline").
• We calculate an evidence_source_count to ensure claims are verified across multiple roles/projects rather than concentrated in a single summary section.

Deterministic Scoring

The system is 100% deterministic. Even the natural language reasoning generation (which features thousands of possible sentence combinations) uses a stable zlib.crc32 hash derived from candidate_id, title_class, and score_bucket to seed the template selection.

This guarantees:

1. Identical inputs always produce byte-for-byte identical output files.
2. Hallucinations are structurally impossible because templates interpolate variables directly from verified profile data.

Runtime

~21 seconds to process and rank all 100,000 candidates. Complexity is O(N) where N is the number of candidates.

Memory Usage

~1.9 GB peak memory. Memory footprint is driven by loading the entire JSON-lines file into an internal array. This is safely under the 16 GB competition limit.

How to Run

python rank.py \
--candidates candidates.jsonl \
--jd job_description.md \
--out submission.csv

Validation

Verify that your submission.csv is fully compliant with the Hackathon's structural specifications:

python validate_submission.py submission.csv

Testing

Run the automated test suite to verify internal assertions and logic (requires pytest):

pytest

Repository Structure

README.md                     # You are here
LICENSE                       # MIT License
requirements.txt              # Environment dependencies
Dockerfile                    # Reproducible environment container

ARCHITECTURE.md               # Additional architectural design notes

rank.py                       # Main orchestrator script
scorer.py                     # Rules and reasoning template generator
features.py                   # Keyword extraction and feature engineering
jd_requirements.py            # Static taxonomy for the target role

candidate_schema.json         # Reference schema for candidate inputs
job_description.md            # The specific job description being matched

validate_submission.py        # Validates structural integrity of submission.csv
test_features.py              # Tests feature extraction logic
test_reasoning.py             # Verifies reasoning determinism and diversity
run_audit.py                  # End-to-end performance and diversity audit

submission_metadata.yaml      # Final metadata for submission
.gitignore                    # Local excludes