Senior Quantitative Developer

Senior Quantitative Developer Machine Learning & Regulatory Credit Risk

Location: Westerville, OH (Hybrid 3 days onsite)

Type: Contract

Key Responsibilities:

• Develop and implement regulatory credit risk models (PD, LGD, EAD) using Python, Spark (Scala), and distributed systems in a Kubernetes-based Azure environment.
• Build scalable ML pipelines integrated with MLflow, CI/CD (Azure DevOps), and model governance frameworks.
• Create model explainability layers using tools such as SHAP, LIME, or custom counterfactual frameworks to support model governance and audit.
• Participate in the lifecycle of CECL and CCAR models, including data preparation, feature engineering, model development, and documentation for Model Risk Governance (MRG).
• Partner with data engineers and risk modeling teams to ingest, process, and version complex credit datasets from enterprise systems.
• Conduct model validation, robustness testing, scenario analysis, and performance monitoring in compliance with SR 11-7, OCC, and Fed requirements.
• Lead efforts to incorporate alternative and unstructured data sources, including text analytics and ESG data, into existing model frameworks.

Required Skills & Experience:

• 10+ years in quantitative development or model risk analytics, preferably in banking, regulatory modeling, or enterprise risk domains.
• Advanced expertise in:
• Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow)
• Apache Spark (Scala) for distributed ML workloads
• Azure Kubernetes Services (AKS), Terraform, MLflow

• Deep understanding of U.S. regulatory frameworks: Basel III/IV, CECL, SR 11-7, SR 15-18/19, and CCAR.
• Proven experience building interpretable ML models and documenting them for use in audited and regulated environments.
• Strong communication skills for cross-functional collaboration with MRG, internal audit, compliance, and technology teams.
• Degree in a quantitative discipline such as Mathematics, Computer Science, Financial Engineering, or Statistics (PhD or Master s preferred).
• Prior work with regulatory capital model development or validation teams.
• Familiarity with risk modeling architecture, tools, or data pipelines (Athena, Quartz).
• Experience implementing AI/ML model fairness, bias detection, and transparency controls in regulated environments.
• Participation in regulatory exams (OCC, Federal Reserve, FDIC) or model submission cycles.
• Background in text mining, survival modeling, or NLP for financial documents is a plus.