Director, Data Science

Are you passionate about pricing in the insurance industry? Would you like to join a team dedicated to accelerating our most critical pricing program deployments across all our retail lines nationwide?

The US Retail Market (USRM) Data Science Pricing Deployments team is seeking a Director II, Data Science to lead a team responsible for supporting the design and implementation of our pricing program deployments across all lines in US Retail Markets. This team will deliver expert guidance on complex designs, promote consistent and effective implementations across products and lines, and leverage best practices while providing the necessary resources to support Earnix deployments throughout the organization. Ultimately, this role will be key to scaling the speed and efficiency of implementing pricing programs in Earnix at Liberty Mutual.

Roles and responsibilities:

• Design Excellence and on-demand support

• Support Earnix design teams by promoting consistent implementation of rating programs across Lines of Business and product lines to ensure alignment and uniformity.
• Offer expert support for complex and sophisticated rating design challenges.
• Provide additional capacity to support high-priority initiatives such as migrations, builds, and ad-hoc requirements across teams.

• Best Practices and Process Improvement
  • Collaborate with Earnix Design teams to establish best practices for rating program designs, driving continuous improvement in quality and efficiency.
  • Enforce rigorous design documentation standards, ensuring all decisions are thoroughly documented with background, rationale, and impact for transparency and future reference.
  • Drive continuous improvement and innovation by identifying opportunities to streamline processes, reduce manual effort, and leverage automation in pricing deployments.
  • Work across design teams to support awareness and adoption of new tools & processes.
• Training & Development
  • Develop training materials and deliver workshops to upskill teams on Earnix functionalities, best practices, and emerging tools.
• Earnix Platform Adoption & Innovation
  • Proactively explore new features and tools within Earnix, leading adoption efforts that add meaningful value.
  • Gather requirements and provide expert guidance for enhancements and new capabilities within the Earnix platform.
  • Expand Earnix’s use as an analytical tool by designing innovative applications beyond standard rating programs to drive business value.
  • Assist Earnix users in developing effective visualizations within the Earnix analytical environment to improve decision-making and communication.
• Community engagement
  • Maintain active involvement with internal data science communities and lead cross-functional groups to foster knowledge sharing and innovation.

• 2+ years of proven experience creating rating logic and implementing rating programs in tools like Earnix, Radar Live or IBM Modeler
• Strong understanding of rating algorithm structures, including their components, inputs, and outputs.
• Strong knowledge of predictive analytic techniques and statistical diagnostics of models.
• Strong knowledge of data science tools, with proficiency in Python for data science applications
• Proven ability to lead and drive projects and assignments to completion through others, to include prior experience effectively managing talent
• Demonstrated ability to exchange ideas and convey complex information clearly and concisely, verbally, visually (presentations and exhibits), and in writing, consistent with relevant audience and stakeholders
• Demonstrated skill in persuading and influencing diverse stakeholders to gain buy-in and foster collaboration
• Ability to establish and build relationships within the organization.
• Ability to give effective training and presentations to management and other groups.
• 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 an advanced degree (in Statistics, Mathematics, Data Science or other relevant field of study) and 5 to 7 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) with 10 years of relevant experience. Insurance or financial services industry experience preferred.