Director I, Data Science, Enterprise Data & Data Science

About Liberty Mutual Insurance At Liberty Mutual, we believe progress happens when people feel secure. For more than 110 years we have helped people and businesses embrace today and confidently pursue tomorrow by providing protection for the unexpected and delivering it with care. A Fortune 100 company with more than 40,000 employees in 28 countries and economies, we are the ninth largest global property and casualty insurer and generate more than $50 billion in annual consolidated revenue. We operate through three strategic business units: US Retail Markets, providing auto, home, renters and other personal and small commercial lines property and casualty insurance to individuals and small businesses countrywide; Global Risk Solutions, delivering a full range of comprehensive commercial and specialty insurance, reinsurance and surety solutions to mid-size and large businesses worldwide; and Liberty Mutual Investments, deploying more than $100 billion of long-term capital globally across its integrated platform to drive economic growth, power innovation and secure Liberty Mutual’s promises. For more information, visit www.libertymutualinsurance.com.

Liberty Mutual Insurance

Pursue your tomorrow, today.

Insurance

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libertymutualgroup.com

10,001+ employees

Insurance

Boston, MA

Privately Held

563k LinkedIn followers

Raised $350M

• We're seeking an exceptional, hands-on Data Scientist with deep expertise in data science, MLOps, and building GenAI solutions to join our Enterprise Data & Data Science team. In this role, you'll identify, evaluate, and develop solutions at the intersection of data science, generative AI, and enterprise data strategy—including GenAI evaluation frameworks and semantic data layers. You'll also serve as a community champion, driving the adoption of best practices across a large, collaborative data science organization.
• Design, build, and evaluate generative AI solutions and agentic systems
• Develop and maintain data semantic layers and knowledge graphs for enterprise-scale data accessibility
• Build evaluation frameworks and tools to assess GenAI systems' performance, reliability, and safety
• Collaborate with other data scientists, machine learning engineers, data engineers, and business partners
• Champion best practices and tooling across a broad data science community
• Lead cross-functional working groups and contribute to innovation in AI/ML methods
• Communicate complex technical findings clearly to non-technical stakeholders
• Serve as a technical consultant on complex, high-impact projects

Required

• Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
• Advanced knowledge of predictive toolset; reflects as expert resource for tool development.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Ability to establish and build relationships within and outside the organization.
• Ability to give effective training and presentations to management and other groups.
• Ability to use results of analysis to persuade team, department management or senior management to a particular course of action.
• Broad knowledge of business drivers and market context.
• Has a value driven perspective with regard to understanding of work context and impact.
• Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 6 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of 8 years of relevant experience.

Preferred

• Strong foundation in data science and machine learning
• Proven MLOps expertise across the complete data science lifecycle
• Experience building GenAI solutions and agentic systems
• Familiarity with GenAI evaluation methodologies
• Experience with data semantic layers and/or knowledge graphs
• Track record of cross-functional collaboration in a large enterprise environment
• Ability to work ET hours

• Flexible Time Off
• Professional Development Opportunities
• Employee Resource Groups
• Comprehensive Benefits