The GRM Product Design and Modeling Business Lines Package & WC team has an opening for a Director I (STP), Data Science, who will help lead development and execution of rating & pricing models for the Property & General Liability lines of business. This individual contributor role will report directly to the Package & WC Design Lead and will have opportunities to collaborate directly with global teams in Product Design and Modeling, including Complex Components, Demand Modeling, and Pricing Sophistication; opportunities may also exist to work on projects globally in partnership with the East & West Design team.
The Director role will require collaboration with business partners to develop best-in-class new business pricing sophistication. This level reflects broad knowledge of Business Lines products, actuarial & data science techniques, and the application of those techniques the Property & General Liability lines of business.
- Build complex loss cost models for Property & GL lines of business, incorporating learnings from other Business Lines as appropriate
- Build or incorporate complex sub-models to improve accuracy
- Explore and utilize advanced modeling techniques (e.g. machine learning techniques such as Elastic Nets)
- Collaborate with Delivery, Underwriting Effectiveness, and other business partners to help implement new pricing programs
- Interpret and provide a clear view of model results
- Understand the competitive marketplace, business issues, and data challenges to deliver actionable insights, recommendations and business processes
- Effectively communicate results in written, oral, and presentation formats
- Mentor other team members
- Bachelor's degree required. Insurance designations such as FCAS or advanced degree in a quantitative field desirable.
- Overall 5-7 years of progressively more responsible experience.
- Strong analytical skills with solid understanding of all casualty actuarial techniques, standards, and assumptions. Expert skills in Excel, PowerPoint, and statistical software packages (e.g., SAS, Emblem, R, Python) highly desired.
- Proficient in predictive analytics including real-world experience in model development, validation, and testing.
- 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., SQL) preferred.
- Demonstrated ability to exchange ideas and convey complex information clearly and concisely, both verbally and in writing.
- Ability to establish and build effective relationships within and outside the organization.
- Ability to perform high-level work both independently and collaboratively as a project team member or leader.
At Liberty Mutual, our purpose is to help people embrace today and confidently pursue tomorrow. Thats why we provide an environment focused on openness, inclusion, trust and respect. Here, youll discover our expansive range of roles, and a workplace where we aim to help turn your passion into a rewarding profession.
Liberty Mutual has proudly been recognized as a Great Place to Work by Great Place to Work US for the past several years. We were also selected as one of the 100 Best Places to Work in IT on IDGs Insider Pro and Computerworlds 2020 list. For many years running, we have been named by Forbes as one of Americas Best Employers for Women and one of Americas Best Employers for New Graduatesas well as one of Americas Best Employers for Diversity. To learn more about our commitment to diversity and inclusion please visit: https://jobs.libertymutualgroup.com/diversity-inclusion
We value your hard work, integrity and commitment to make things better, and we put people first by offering you benefits that support your life and well-being. To learn more about our benefit offerings please visit: https://LMI.co/Benefits
Liberty Mutual is an equal opportunity employer. We will not tolerate discrimination on the basis of race, color, national origin, sex, sexual orientation, gender identity, religion, age, disability, veterans status, pregnancy, genetic information or on any basis prohibited by federal, state or local law.