Senior Analyst/Associate, Quantitative Solutions Liberty Mutual Investments

Job Title

Quantitative Solutions Team Member

Job Description

Liberty Mutual Investments (LMI) is the investment firm for Liberty Mutual Group (Liberty). With deep expertise in fixed income, equity, and alternative strategies, LMI invests more than $100B of long-term capital globally, and has a team of nearly 300 investment, finance, and operations professionals located in Boston, MA, and New York, NY. LMI has a clear purpose: drive economic growth, build enduring businesses side-by-side with our partners, and generate superior risk-adjusted returns that secure Liberty's promises.

LMI's Quantitative Solutions team, part of the Global Strategy & Capital Allocation (GSCA) business unit, is seeking a highly motivated, entrepreneurial individual. The Quantitative Solutions team leads the General Account's asset allocation and portfolio construction process, delivering advanced analytics, scenario analysis, and forward-looking insights to guide long-term, annual, and tactical investment decisions within regulatory, rating agency, and portfolio constraints.

This role focuses on building and advancing the team's quantitative modeling and research capabilities across public and private asset classes. The successful candidate will develop models and analytical tools that deepen the team's understanding of return drivers, risk characteristics, and portfolio dynamics across the General Account's full investment universe. A distinctive feature of this role is the opportunity to work at the intersection of top-down asset allocation and bottom-up mandate execution, helping bridge the analytical gap between how the portfolio is designed and how it is managed. This is a research-oriented role suited to someone with genuine intellectual curiosity, strong quantitative foundations, and comfort working with the diverse and often incomplete data that characterizes institutional multi-asset portfolios.

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 PitchBook, 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

Qualifications

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. The ideal candidate must also bring the following qualifications:

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