Senior Quantitative Developer Machine Learning & Regulatory Credit Risk

Job Title: Senior Quantitative Developer Machine Learning & Regulatory Credit Risk

Requirement ID: 94518

Location: Westerville, OH

(Hybrid 3 days onsite)

Client: JPMorgan Chase & Co.

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.