Senior Analyst, Data Science

Liberty Mutual Insurance • Boston, MA

64d

About The Position

Join Liberty Mutual as an Analyst II, Data Science on the Service Data Science team within US Retail Markets Insights & Solutions organization and help shape how millions of customers and agents interact with insurance. You’ll develop models and collaborate closely with product, operations, and engineering to deploy models and insights that increase retention, unlock cross-sell value, and boost operational productivity through customer segmentation, call center routing, agent assist recommendation, and more. This is a hands-on role working with large, diverse datasets and modern tools; it’s a great opportunity to learn, grow, and see your work drive measurable business outcomes. Examples include: Predictive modeling and segmentation —Build classification and regression models to predict customer intents, behaviors, and outcomes (e.g., logistic regression, gradient boosted trees etc.). Uplift and causal inference — Design experiments and estimate the causal effect of interventions (e.g. T Learner, DoubleML, instrumental variables, propensity score methods etc.). Simulation and decision analysis to quantify the expected cost/benefit. **This role may have in-office requirements based on candidate location** **Level of position offered will be based on skills and experience at manager discretion**

Requirements

• Solid knowledge of predictive analytics techniques and statistical diagnostics of models.
• Advance knowledge of predictive toolset; expert resource for tool development.
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
• Has a value-driven perspective with regard to understanding of work context and impact.
• Competencies typically acquired through 0-1 yrs. of related experience with a Ph.D., a minimum of 2-3 yrs. of experience with a master’s degree, a minimum of 4+ yrs. of experience with a bachelor’s degree.

Responsibilities

• Manipulate large data sets and create predictive models (including data ingestion, feature engineering, validation, explainability, and production deployment, in partnership with product, operations, and engineering teams)
• Create and test hypotheses, design A/B experimentation, analyze results and statistical significance
• Conduct model performance evaluation or valuation sizing
• Follow ML Ops best practices to create organized code repos, production-quality code, and reproducible results
• Communicate quantitative analyses into easy-to-understand stories and actionable insights and recommendations to stakeholders
• Coach and mentor junior data scientists and foster a culture of continuous learning
• Regularly engage with the data science community, share knowledge, learn best practices and participate in cross functional working groups

Benefits

• Comprehensive benefits and continuous learning opportunities
• Strong relationships and support for life and well-being

What This Job Offers

• Career Level: Entry Level
• Education Level: Master's degree
• Number of Employees: 5,001-10,000 employees