Senior Quantitative Developer Machine Learning & Regulatory Credit Risk

Overview

Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)

Skills

Quantitative
Python
Apache Spark
AKS
Terraform

Job Details

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

Location: Westerville, OH (Hybrid 3 days onsite)

Type: Contract

6 Positions

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.

Regards,

Manoj Kumar, Senior Recruiting Manager

Data Systems Integration Group, INC
Email: ,

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