Assistant Director, Data Science, Small Commercial Product Design & Modeling

Liberty Mutual Insurance•Boston, MA

About The Position

Join the Small Commercial Product Design & Modeling (PDM) department to develop industry-leading pricing and underwriting models. We pride ourselves in developing the best-in-class models and making the team an awesome place to work!? The Small Commercial PDM team turns data science insights into business value and partners closely with business stakeholders to expertly align risk with rate. Responsible for the price segmentation of a portfolio in excess of $4 billion, Small Commercial insurance team members of varied backgrounds execute on a wide array of critical initiatives. Roles within the team range from leaning more heavily into research to inform our product design, to directly interfacing with regulators and Profit & Loss owners. Our team members make an impact by improving model fits, increasing the speed of model deployments, improving capabilities to test, calibrate, and monitor models, and serving as technical experts to explain our models to the broader Small Commercial organization. With opportunities for growth, mentorship, and engagement with the data science community, we offer a stimulating environment for those with a passion for technical excellence and problem-solving. The Small Commercial Product Design and Modeling team is hiring an individual contributor to work on predictive modeling for our core programs. The ideal candidate is proactive and intellectually curious, highly technical, and can efficiently think through complex business questions independently. The role will have dual responsibilities of conducting modeling research to help enhance Small Commercial Lines while also leading the development and execution of rating & pricing models for the Specialty lines of business. The person in this role will report to the cross lines research and support manager and also have opportunities to collaborate directly with teams in US Data Science, including DSE and Infrastructure. This position may have in-office requirements dependent upon candidate location.

Requirements

Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
Expert knowledge of predictive toolset; reflects as expert resource for tool development.
Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
Networks with key contacts outside own area of expertise. Ability to establish and build relationships within the aligned functional area or SBU.
Ability to give effective training and presentations to peers, management and less senior business leaders.
Ability to use results of analysis to persuade team or department management to a particular course of action.
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 2 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 4 years of relevant experience or may be acquired through a Bachelor`s degree(scientific field of study) and a minimum of 5+ years of relevant experience.

Responsibilities

Conduct Cross Lines research to improve modeling accuracy and best practices across all Small Commercial Core lines of business
Independently build or incorporate complex sub-models to improve accuracy
Explore and utilize advanced modeling techniques
Collaborate with Delivery, Underwriting Effectiveness, and other business partners to help implement new pricing programs
Enable the business to make clear tradeoffs among choices, with a reasonable view into likely outcomes.
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
Act as a mentor and help develop less experienced analysts on the team
Regularly engage with the data science community and lead cross functional working groups.
Identify new strategic opportunities for use of theoretical methods and tools.