Currently, we are partnered with a tier one investment bank who is seeking an Assistant Vice President-level Quantitative Strategist to join its Wealth Management business. This individual will be responsible for developing and maintaining forecasting, valuation, and risk models supporting a large mortgage and lending platform. The role offers broad exposure across quantitative research, balance sheet analytics, stress testing, and product modeling while partnering closely with business, risk, and technology teams.
Responsibilities
- Develop econometric and statistical models used to forecast lending balances, product revenues, and portfolio performance
- Build and enhance valuation, forecasting, and risk management models for mortgage and lending products
- Analyze large datasets to identify key drivers of portfolio behavior and business performance
- Design, test, and implement quantitative models using Python and other programming languages
- Perform model validation support, back-testing, monitoring, and ongoing model enhancements
- Document modeling methodologies and communicate findings to business stakeholders and model governance teams
- Partner with technology teams to productionize model frameworks and improve analytical processes
- Support regulatory, stress-testing, and broader risk management initiatives
- Collaborate with cross-functional partners across quantitative analytics, finance, risk, and lending businesses
Qualifications
- Master's or PhD in Statistics, Mathematics, Physics, Economics, Engineering, Data Science, Quantitative Finance, or a related quantitative discipline
- 3-7 years of experience developing quantitative, econometric, or statistical models within financial services
- Strong programming skills in Python required; experience with C++ is highly preferred
- Experience working with forecasting, valuation, risk, or balance sheet models
- Strong knowledge of statistical modeling and quantitative research techniques
- Ability to communicate complex quantitative concepts to both technical and non-technical audiences
- Experience with mortgage, consumer lending, retail banking, or wealth management products is preferred
- Familiarity with stress testing, model governance, or regulatory modeling frameworks is a plus
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