About the role

Senior Data Scientist developing ML and AI solutions for The Hartford's Claims Data Science team. Collaborating to improve customer journey and drive efficiency in operations.

Responsibilities

Design, develop, and robustly evaluate LLM‑powered solutions, including prompt engineering, agent‑based workflows, and retrieval‑augmented generation (RAG), to achieve financial objectives, solve business problems, and identify long term opportunities that improve the customer journey
Collaborate and partner with business stakeholders in a way that supports the vision and sustains a culture that treats analytics as a corporate asset
Lead execution of machine learning and applied AI solutions, including traditional predictive models and generative AI use cases, in close collaboration with data science, engineering, and business partners
Assist in identifying and assessing the value of new data sources and analytical techniques to ensure ongoing competitive advantage
Contribute to successful implementation of strategies to achieve targeted business objectives
Develop knowledge of The Hartford's formal and informal structures, business processes, and data sources in your area of expertise
Remain current on research techniques and experiment with state-of-the-art tools applicable to the team’s function (e.g., new modeling approaches, LLM capabilities)
Provide economic, qualitative, and statistical support to ensure model outputs and AI‑driven recommendations are accurate, interpretable, and actionable for business decision‑making
Learn/bring best practices to guide the direction of our Data Science and Data Engineering workflows

5+ years of relevant experience recommended
Master’s or Ph.D., or equivalent experience, in Statistics, Applied Mathematics, Quantitative Economics, Actuarial Science, Data Science, Computer Science, or a similar analytical field
Proficiency in statistical modeling, inference, experimentation, and building machine learning algorithms in Python
Proficiency in SQL and navigating databases to extract relevant attributes
Proficiency with Unix and Git and best practices in managing codebases
Proficiency in the end-to-end analytical solution lifecycle, from requirements gathering to monitoring and production validation
Experience building modeling solutions in cloud-native environments, such as Google Cloud Platform, a plus
Experience with software development and/or agent development a plus
Able to communicate effectively with both technical and non-technical teams
Able to translate complex technical topics into business solutions and strategies, as well as turn business requirements into a technical solution
Experience with leading project execution and driving change to core business processes through the innovative use of quantitative techniques
Candidate must be authorized to work in the US without company sponsorship.