Director, Data Science

The creative problem solvers in Liberty Mutual’s Insights & Solutions group harness the power of data, analytics and technology to create innovative solutions that advance our business, make an impact on our industry and promote individual careers. Insights & Solutions’ cross-functional team interacts with every area of our enterprise, challenging the status quo and helping people embrace today and confidently pursue tomorrow.

The US Retail Markets Data Science team brings together a diverse range of talent to predict future risk and what our customers will need to recover. Our data engineers write code that turns trillions of bits of information into structured data—data that our hundred-plus Data Scientists analyze with cutting-edge modeling techniques to unlock insights. From there, our tools and deployment teams ensure this data can be practically applied to business problems across US Retail Markets. Join us and be a part of this dynamic group driving industry-leading data segmentation, fueling the team’s success now and into the future.

Join Demand and Decision Science to lead cutting-edge modeling efforts that shape customer retention, pricing segmentation, and strategic business decisions for US Personal Lines Property & Specialty. You’ll combine technical expertise with business insight, delivering actionable recommendations and innovative solutions that drive organizational success.

The USRM Data Science Demand and Decision Science team is seeking two independent, creative, and self-motivated individual contributors to work on the US Personal Property & Specialty line of business. In these roles, you will build complex demand models for customer retention and conversion. You will also spearhead the development of expense-based pricing segmentation models and guide critical pricing program design decisions. Additionally, you will leverage causal inference techniques to enable more informed and high impact organizational decisions and conduct detailed cost benefit analyses to assess various strategic options. You will also enhance demand-based calibration tools that support new rating program rollouts. 

**This role may have in-office requirements based on candidate location.*

Responsibilities: 

• Build complex retention, conversion, and price elasticity models using various techniques including GLMs, machine learning, and causal inference techniques. 
• Generate data-driven answers to open-ended strategic questions related to customer demand and present out on recommendations to leadership.  
• Mine large data sets using sophisticated analytical techniques to generate insights and inform business decisions.  
• Responsible for large, sophisticated components of highly complex projects.  
• Guide aspects of project design as a technical consultant for the team.  
• Enable the business to evaluate trade-offs between and among choices, with a reasonable view into likely outcomes.  
• Drive innovation in the application and development of tools and practices.  
• Identify and test hypotheses, ensuring statistical significance, and build predictive models for business application.  
• Translate quantitative analyses and findings into accessible visuals for non-technical audiences, providing a clear view into interpreting the data.  
• Customize analytic solutions to specific client needs.  
• Regularly engage with the data science community and lead cross functional working groups. 

The ideal candidate will have:

• 5-7 years in jobs with progressive responsibility, preferably in P&C insurance 
• Bachelor’s degree required, preferably in math, statistics, economics, or other quantitative fields of study 
• Strong analytical skills with solid understanding of predictive modeling concepts and techniques 
• Advanced skills in statistical software packages (e.g., Python, R, and Emblem) 
• Strong knowledge of insurance operations and the procedures of Financial, Underwriting, Claims, Statistical, Information Technology, Legal, and Sales departments 
• High-level knowledge of data sources, tools, and the business (lines, systems, pricing plans), predictive modeling, and code (e.g., Python, SQL) preferred 

• 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.