Sr. Quant Data Modeler – SAS

Job Title: Sr. Quant Data Modeler – SAS Client : TD Bank Location : Wilington, DE (Hybrid) Duration : 12 Month+ Pay : $68/Hr W2 ($5 Referral) MUST HAVE: • 7+ years in predictive model development (regression, ML techniques) • Financial services/banking experience, including stress testing (CCAR, DFAST) and model governance • Proficient in SAS and Python • Strong communication and presentation skills SUMMARY OF DAY-TO-DAY RESPONSIBILITIES Job Title: Sr. Quant Data Modeler – SAS Description: This position sits within the PPNR Model Development team under the Treasury and Financial Modeling group. The role will lead quantitative analysis and the development, implementation, and execution of statistical models to forecast balance sheets for regulatory stress testing purposes. Key Responsibilities: • Interpret and translate business requirements into technical solutions that meet modeling and forecasting needs • Analyze and manipulate large datasets; design econometric models to forecast loan and deposit balances based on macroeconomic variables • Drive the design, planning, implementation, and testing of modeling initiatives and cross-functional projects • Engage stakeholders to review and defend model assumptions and methodologies, and respond to challenges for approvals • Produce clear, well-structured documentation for models and methodologies • Write and maintain efficient, production-quality code for modeling and data processing tasks Must Have: • 7+ years of experience in predictive model development using both traditional regression and machine learning techniques • Deep understanding of financial services (commercial and retail lending), regulatory stress testing (CCAR, DFAST), and model governance standards • Proficiency in SAS and Python for statistical analysis and model development • Strong communication skills, with the ability to present complex models to non-technical audiences Nice to Have: • Experience working in Unix/Linux and Microsoft Azure environments • Familiarity with relational databases, big data frameworks, and SQL • Interest and aptitude for applied quantitative research Req Qual Notes: Core Responsibilities: • Lead model development efforts focused on identifying patterns in statistical and time-series data. • Design and maintain stress testing models in compliance with regulatory expectations (e.g., CCAR, DFAST). • Conduct advanced quantitative analysis to develop models supporting financial risk and forecasting. • Extract actionable insights and patterns from large datasets. • Participate in the complete data validation process, ensuring alignment with internal model governance frameworks. • Work with cross-functional stakeholders including model governance, compliance, and regulators. Key Requirements: • Advanced statistical background (not from a general CS/engineering degree). • Proficient in SAS programming with solid statistical coding skills; Python and SQL also required. • Strong knowledge of time-series analysis and data modeling techniques. • Familiarity with model governance processes and working with regulatory bodies. • Experience in modernization efforts or evolving data/modeling platforms (preferred). • Demonstrated ability to critically analyze problems and propose/implement data-driven solutions. • Graduate degree required; PhD in economics, statistics, finance, or a related quantitative discipline • Prior experience in the financial industry, especially in stress testing or regulatory modelling.