Quantitative Solutions – Liberty Mutual Investments

Responsibilities

• Develop new and maintain existing quantitative models and analytical tools that support asset allocation and portfolio construction decisions under insurance, regulatory, and rating agency constraints.
• Produce scenario analysis, stress testing, and portfolio analytics to inform long-term, annual, and tactical allocation decisions.
• Contribute to the enhancements of existing and new quantitative models for public and private asset classes — including private equity, private credit, real assets, and infrastructure.
• Develop analytics that further enhance GSCA's capabilities to monitor asset allocation execution through internal mandates.
• Support the development and integration of commitment pacing, cash flows, and NAV forecasting models that integrate private markets into the total portfolio construction and glidepath allocation modeling.
• Source, evaluate, and integrate private markets data from providers such as Pitch Book, Preqin, Burgiss, and internal systems, building clean and reliable data pipelines and analytics.
• Participate in GSCA cross-team research projects across portfolio strategy and total portfolio management contributing to the team's collective knowledge base and decision-making process.
• Partner with LMI Investment Business Units to institutionalize analytics and data.

• The position requires a Master’s degree in Financial Engineering, Statistics, Mathematics, Economics, Operations Research, or a related field plus 2+ years of experience in a quantitative research or related role.
• Working knowledge of fixed income assets, public equities, and private markets, with exposure to private asset classes such as private credit, real assets, infrastructure, or private equity.
• Strong applied quantitative skills, including experience with simulation techniques, statistical modeling, time series analysis, and optimization, with the ability to build and maintain production‑ready research tools.
• Demonstrated knowledge of private asset classes, including private equity, private credit, real assets, or infrastructure, with an understanding of fund structures, cash flow mechanics, performance metrics (IRR, MOIC, DPI, TVPI), and the challenges of working with private markets data.
• Understanding of how private assets integrate into a total portfolio context, including the interplay between illiquidity, commitment pacing, vintage diversification, and portfolio-level risk and return.
• Advanced programming and data skills in Python and SQL, with experience using version control (Git) and comfort working with large, messy, and non-standardized datasets typical of private markets.