Senior Machine Learning Engineer, Model Risk Management

What you'll do

• Independently challenge model owners across lending, fraud, and AML by reproducing results and setting acceptance thresholds.
• Identify and prove "silent errors" in models before they reach production environments.
• Develop evaluation methods that account for rare events, shifting populations, and post-launch drift.
• Write and ship production code in existing codebases to build tools for model validation.
• Build agentic validation tooling to orchestrate parallel processes for evaluating AI systems.
• Evaluate ML systems end-to-end by analyzing how features, training, serving, and monitoring integrate.
• Link explainability and fair-lending findings to specific product decisions and consumer credit models.
• Establish standards and protocols for validating frontier production AI and agentic systems.

What we're looking for

• A quantitative degree or equivalent experience in building or validating models in high-stakes domains like credit, fraud, or financial crime.
• Mastery of effective-challenge methodology including reproduction, conceptual-soundness review, benchmarking, stress testing, and outcomes analysis.
• Deep applied knowledge of ML and statistics across model families from regression and tree ensembles to deep learning.
• Experience with experimentation and statistical rigor, including holdout design, reasoning about uncertainty, and evaluating models beyond aggregate accuracy.
• Proficiency in production-quality Python and SQL for working with large datasets and building reproducible code.
• Fluency with modern AI, specifically building with LLMs and agentic tools while judging when outputs can be trusted.
• Familiarity with model risk management frameworks and fair-lending standards.
• Strong communication skills to explain and defend technical conclusions to model owners and senior stakeholders.