Data Science Intern

Plymouth Rock Assurance•Boston, MA

9d•$30 - $35•Onsite

About The Position

The Data Science Intern/Co-op will support research and development of predictive models and analytical techniques related to insurance pure premiums, customer lifetime value, customer buying behavior, and other topics. The work uses internal and external data to inform pricing, underwriting, and product strategies across the Plymouth Rock Group of Companies. Analyses and models will be tested through broad implementation and pilots so approaches can be evaluated and refined. The intern will analyze data to identify drivers of expected insurance losses and customer lifetime value, learn relevant data sources, elicit hypotheses from internal personnel, and test those hypotheses. The role also contributes to developing and enhancing models, advancing modeling capabilities, and transferring those capabilities to product management personnel. Working with the product community, the intern will help apply models in regulatory and competitive contexts. This includes making sure models are understood, assessing their implications, defining implementation plans, validating results, and making timely adjustments. The position is an in-office internship/co-op in Boston, Massachusetts, starting January 2027. The hourly pay range is $30–$35, with actual compensation dependent on multiple factors. Plymouth Rock and its affiliated companies write and manage more than $2 billion in personal and commercial auto and homeowners insurance across the Northeast and mid-Atlantic. The group employs more than 1,900 people and is headquartered in Boston.

Requirements

Graduate student in economics, statistics, or a related field; doctoral study is preferred.
Practical experience in econometrics and statistics, preferably in a business context.
Strong written and oral communication skills, including the ability to communicate technical concepts to technical and non-technical audiences.
High standards for work products.
Creative thinking and ability to synthesize information from various sources and apply it to concrete business problems.
Flexibility and resiliency characteristic of a professional.
Proficiency with a modeling programming language such as SQL, R, SAS, or Python.

Nice To Haves

Background in Bayesian statistics preferred.
Knowledge of and experience with the technical aspects of insurance is a plus.
Doctoral study is preferred.

Responsibilities

Analyze internal and external data to identify drivers of expected insurance losses and customer lifetime value.
Understand relevant internal and external data sources, elicit hypotheses from internal personnel, and test those hypotheses using available data.
Help develop and enhance models used within the product management structure.
Advance modeling capabilities and transfer modeling advancements to product management personnel.
Work with the product community to facilitate model application in regulatory and competitive contexts.
Ensure models are understood and their implications are assessed appropriately.
Define implementation plans, validate results, and make adjustments rapidly.