Senior Machine Learning Engineer, Model Risk Management

📋 Description

• Challenge model owners across lending/fraud/AML; reproduce results; set thresholds.
• Find silent errors that mislead metrics; prove them before production.
• Robust evaluation for rare events, drift, and shifting populations.
• Work in unfamiliar codebases; learn data/configs; ship production tooling.
• Build agentic validation tooling; orchestrate parallel agents.
• Reason about ML systems end-to-end; evaluate and challenge design.

🎯 Requirements

• Quant degree or equivalent; senior-IC depth in high-stakes models (credit/fraud).
• Strong evaluation: reproduction, benchmarking, stress testing, outcomes analysis.
• Deep ML/statistics across model types: regression, trees, deep learning; calibration.
• Experimental design and statistics: holdout, uncertainty, beyond-accuracy evaluation.
• Production-grade Python, SQL on large datasets; reproducible, tested code.
• Experience with LLMs and agent tools; judge when outputs are trustworthy.
• Familiarity with model risk frameworks and fair-lending standards.

🎁 Benefits

• Remote work options
• Medical insurance
• Flexible time off
• Retirement savings plans