Required Qualifications & Skills:

Technical Proficiency: Strong coding skills in languages like Python, and SQL; experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Statistical Expertise: Deep understanding of statistical modeling, probability, and advanced mathematical concepts.
Domain Knowledge: Understanding of financial regulations (e.g., SR 11-7, CECL, CCAR) if working in banking.
Soft Skills: Excellent technical writing and stakeholder communication; the ability to explain complex quantitative risks to non-technical business partners and legal/compliance teams.

Experience: Typically requires 7+ overall and 3-5+ years of experience in data science, risk management, or quantitative analysis

Key Responsibilities:

Model Validation: Conduct independent reviews and testing of quantitative models, including design approach, sampling approach, sensitivity analysis, and robustness checks.
Risk Assessment: Assess models for conceptual soundness, data limitations, and quantitative biases (e.g., fairness and ethical AI standards).
Compliance & Governance: Ensure all modeling lifecycles align with internal MRM frameworks and regulatory mandates.
Effective Challenge: Provide constructive, actionable feedback and challenges to model developers regarding model design and limitations.
Documentation: Author comprehensive validation reports and maintain documentation for audit purposes and senior management review

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Data scientists

Job Description

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