WHAT YOU WILL DO

Quantitative modeling & analysis

• Design, build, and maintain stochastic models and Monte Carlo simulation engines that quantify and explain expected outcomes for stable value wrap programs, insurance guarantees, and 401(k) target date fund (TDF) and total portfolio strategies.
• Perform derivative modeling and pricing to value guarantees and hedge structures embedded in New Markets products, applying appropriate valuation model output.
• Generate standardized outcome and risk metrics (e.g., projected wealth distributions, probability of meeting income targets, downside/tail outcomes, drawdown risk, and sequencing risk) to inform product design and internal oversight.
• Undertake risk/reward assessments and calculate analytical attributes across the product portfolio, making recommendations that contribute to achieving product objectives.

Historical back testing & scenario analysis

• Develop and maintain historical back testing frameworks to evaluate how product structures behave across market regimes and historical periods.
• Perform scenario analysis based on hypothetical stress scenarios or actual historical environments and summarize key movements and drivers for governance and product reviews.
• Perform risk analysis of assets and liabilities utilizing financial modeling and quantitative risk assessments, monitoring holdings relative to appropriate risk sensitivity tolerances.

Stable value & insurance guarantee analytics

• Perform quantitative analysis of stable value wrap programs, including crediting-rate mechanics, market-to-book ratios, and wrap capacity and risk.
• Quantify and monitor the cost and risk of insurance guarantees under stochastic scenarios, supporting hedging and asset-liability management-related activities.
• Develop and analyze spreadsheets and database reports used by product, investment, and risk partners in assessing portfolio risk and return characteristics.

Reproducible research tooling, data & controls

• Build and maintain reproducible analytics and simulation pipelines in Python supported by SQL and AI tools, implementing testing, version control, and parameter traceability.
• Use SQL to source, clean, and transform time series and reference datasets; maintain data lineage and perform data quality checks.
• Maintain standard templates, runbooks, and documentation to ensure consistency, repeatability, and audit readiness for model outputs.