Overview
Skills
Job Details
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.