Job Locations US-MA-Boston

ID

2026-74224

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 $120,000.00/Yr.

Maximum Salary

USD $215,000.00/Yr.

Recruiter

James Schweitzer

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 Associate. 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.

Responsibilities:

• Develop, maintain, and enhance quantitative models and analytical tools that support asset allocation and portfolio construction decisions
• Produce scenario analysis, stress testing, and portfolio analytics to inform long-term, annual, and tactical investment decisions under insurance, regulatory, and rating agency constraints
• Support senior team members in monitoring portfolio positioning, risk budgets, liquidity, and projected investment cash flows
• Build and enhance expected return, risk, and relative-value models across asset classes in line with portfolio objectives and strategy
• Contribute to the further development of the team’s expertise in private assets quantitative modeling, including return drivers, risk characteristics, and portfolio integration
• Support top-down macroeconomic and asset-class research across public and private markets
• Partner with LMI Investment Business Units (IBUs) to support data, analytics, governance and research initiatives that improve consistency, transparency, and decision-making
• Participate in cross-team research projects, sharing insights and contributing to the team’s collective knowledge base
• Develop expertise in insurance-driven portfolio construction, private assets, and asset-liability management considerations through hands-on analysis and collaboration

The position requires a Master’s degree in Financial Engineering, Statistics, Mathematics, Economics, or a related field plus 5+ years of experience in the job offered or a related field. 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
• Understanding of long-horizon, balance-sheet-driven portfolio management, including the interaction between assets, liabilities, liquidity, and capital constraints
• Strong applied quantitative skillset, including time series analysis, simulation techniques, portfolio optimization, and performance attribution, with the ability to translate research into production-ready models and tools
• Advanced programming and data skills, including Python, SQL, version control, and familiarity with AI, machine learning (ML), and modern data architectures and analytics platforms
• Experience with financial and portfolio management systems and tools (e.g., Bloomberg, BlackRock Aladdin, Excel), and comfort working across the full technology project lifecycle
• Familiarity with private asset data providers and datasets and an understanding of their strengths, limitations, and appropriate use cases
• Comfort working with incomplete, noisy, or non-standardized data, applying sound judgment alongside quantitative rigor
• Demonstrated curiosity, ownership mindset, and willingness to proactively identify questions, test ideas, and iterate on models under guidance from senior team members
• Intellectual curiosity about the evolving role of AI in financial services, with a practical interest in experimenting with and applying new tools to improve research, analytics, and decision-making
• Strong communication and collaboration skills, with the ability to explain complex quantitative concepts clearly to a broad range of stakeholders and contribute effectively to cross-functional projects

Sorry the Share function is not working properly at this moment. Please refresh the page and try again later.