Senior Data Scientist / Advisor

Develop advanced mathematical, statistical, econometric, and machine-learning models supporting financial forecasting and risk-management processes. Perform credit-related analysis under expected/base-case and stress scenarios. Develop, evaluate, validate, and interpret predictive and forecasting models. Support financial stress testing, scenario modeling, risk measurement, valuation, and business-performance analysis. Apply statistical modeling techniques from econometrics, statistics, machine learning, computational science, and data optimization. Build predictive analytics capabilities and integrate statistical models and algorithms into business applications. Research and evaluate model results and identify trends, relationships, risks, and business implications. Work closely with Finance, business/product owners, data engineers, platform teams, and other stakeholders. Assess data availability, quality, modeling approaches, and alternative analytical methods. Provide technical direction and review the analytical/modeling work performed by other team members. Develop effective data visualizations, technical documentation, and executive-level presentations. Translate complex quantitative findings into actionable insights for both technical and non-technical stakeholders., Core: Python R SQL Additional Tools: Tableau DBeaver Bitbucket

We are seeking a Senior Data Scientist / Advisor with strong experience in financial forecasting, credit risk, stress testing, econometrics, and advanced statistical modeling. The ideal candidate will combine strong hands-on technical capabilities in Python, R, and SQL with a solid understanding of financial and credit-risk modeling. Experience with DFAST, CCAR, stress testing, scenario analysis, or similar financial forecasting frameworks is highly preferred. This is a senior/advisory role requiring someone who can perform sophisticated quantitative analysis, evaluate model results, guide technical teams, and communicate findings effectively to Finance and business stakeholders., Bachelor’s degree in Data Science, Statistics, Economics, Econometrics, Mathematics, Computer Science, Finance, or related quantitative discipline. Strong professional experience in data science, quantitative analytics, financial modeling, or risk modeling. Advanced programming experience with: Python R SQL Strong experience developing and evaluating statistical, econometric, or predictive models. Experience analyzing large datasets to identify trends, relationships, and business insights. Strong understanding of financial forecasting and/or credit-risk analysis. Experience with financial scenario analysis or stress-testing methodologies. Strong analytical and problem-solving capabilities. Ability to review and provide direction on sophisticated quantitative analysis. Excellent stakeholder-management and communication skills. Ability to present complex analytical concepts clearly to senior business and Finance stakeholders. Must be able to work onsite in Washington, DC three days per week. Must be available for an in-person interview in Washington, DC. Highly Preferred Qualifications Master’s degree or PhD in Data Science, Applied Economics, Econometrics, Statistics, Mathematics, Finance, or a related quantitative discipline. Direct experience with DFAST and/or CCAR stress testing. Experience with credit-risk modeling, financial forecasting, expected-loss modeling, scenario analysis, or financial valuation. Prior experience within banking, mortgage finance, financial services, credit risk, or financial regulatory environments. Experience leading or advising teams of data scientists, quantitative analysts, or modelers. Familiarity with advanced machine-learning and NLP techniques.