Senior Quantitative Developer

Urgent requirement Senior Quantitative Developer Westerville, OH--United States

Job Title : Senior Quantitative Developer
Job Type : C2C
location: : Westerville, OH--United States

Job Description

• 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:

o Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow)

o Apache Spark (Scala) for distributed ML workloads

o 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.