Analyst, Data Science, Earnix Design

Liberty Mutual Insurance•Boston, MA

About The Position

Are you passionate about pricing in the Insurance Industry? Would you like to join a dedicated team responsible for deploying pricing programs across all our retail lines in United States? Then this is your next job! The US Data Science (USDS) Earnix Design Excellence and Support team is looking for an Analyst I/Analyst II/Asst. Director, Data Science, to design and build major pricing programs in Earnix and develop best-in-class practices for pricing deployment design. The Earnix initiative is transforming our pricing ecosystem and post modeling processes to increase speed to market and deploy more sophisticated models across all lines of business. This role will develop deep expertise in deploying, maintaining, and monitoring Liberty Mutual's pricing models, collaborating heavily with data science, product delivery, and tech teams. It will also play a key role in establishing new Earnix capabilities to scale the speed and efficiency of implementing pricing programs and other use cases. This is a ranged posting, level offered will be based on skills and experience at manager discretion. This role may have in-office requirements dependent upon candidate location. USRM Data Science employs over 350 full-time professionals who build advanced data science products and AI solutions for our US Retail Market (USRM) business. By thoughtfully applying data science across USRM functions, we ensure we consistently deliver on our promises while driving sustainable growth across our portfolio. We accomplish this through a focus on model development, deployment, and decision science. In model development, we design sophisticated models that combine deep data science expertise with close collaboration across our go-to-market verticals. This integration ensures our models not only incorporate cutting-edge science but, more importantly, generate measurable business value aligned with each vertical’s strategic goals. Model deployment is equally critical—no matter how advanced a model is, it is ineffective if it cannot be operationalized. Our robust deployment capabilities translate insights into actionable value, prioritizing speed, scalability, and reliability. Beyond driving specific business actions such as pricing policies or underwriting decisions, we also leverage decision science to inform critical strategic decisions. This ensures that data science supports the best possible outcomes not only in daily operations but also in long-term strategy.

Requirements

Solid knowledge of predictive analytics techniques and statistical diagnostics of models.
Foundational/intermediate 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 a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and no professional experience, a Master`s degree (scientific field of study) and 2 to 3 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and 4+ years of relevant experience.

Nice To Haves

Background in insurance pricing.
Strong programming skills in Python and R.
Working familiarity with GLMs and GBMs for predictive modeling.
Strong feature engineering and transformation skills.
Applied use of multi-level or ensemble modeling techniques.
Experience creating rating logic and implementing rating programs in tools like Earnix, Radar Live, etc.

Responsibilities

Design best-in-class capabilities to support post-modeling activities, including calibration, dislocation, and filing.
Build USDS pricing programs in Earnix.
Design and implement other use cases of Earnix as a rating engine.
Ensure accuracy of all pricing and non-pricing outputs from Earnix.
Design and maintain rigorous data models for Earnix analytical projects and API.
As required, troubleshoot and support post launch queries and issues.
Improve and automate processes where applicable.
Engage with the data science community and participates in cross-functional working groups.

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