Job Locations US-MA-Boston | US-NY-New York

ID

2026-76365

Position Type

Full-Time

Job Grade

63

Department

043D-10622 Global Strategy and Capital Allocation

Market

Liberty Mutual Investments (LMI)

Referral Bonus Amount

2500

Minimum Salary

USD $100,000.00/Yr.

Maximum Salary

USD $215,000.00/Yr.

Recruiter

James Schweitzer

Referral Bonus Eligible?

Yes

The Company

Come build on our integrated platform with industry-leading talent, world-class partners, and freedom to innovate.

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 offers the best of both worlds — the look and feel of a boutique investment firm with the reputation and financial strength of a global leader. As the investment firm of a mutual with long-term capital, LMI has a single client mandate. This gives us the freedom to focus on what we do best.

Our portfolio spans a broad spectrum of public and private investments, and we are committed to expanding our capabilities and our toolkit in support of our mission. We invest across diverse asset classes, financial structures, and industries, including real estate, digital infrastructure, healthcare, renewable energy, and technology—with the aim of creating value and powering innovation. We pride ourselves on our extensive network of mutually beneficial partnerships, and we use our substantial influence, capital, and energy to drive towards a better future. #LMI

The Position:

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.

Our broader team, GSCA, is the nerve center of LMI, responsible for developing tools and insights for designing business & investment strategy, deconstructing the macro/market narrative, informing top-down house views, and setting the asset allocation for the General Account (GA). GSCA functions as the Office of the Chief Investment Officer (CIO), influencing all aspects of the portfolio management process and ensuring that LMI delivers for its Liberty partners.

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

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

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