We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. The Hartford seeks a Sr. Data Scientist to join the Claims Data Science team in developing machine learning and artificial intelligence solutions across a range of strategic initiatives. The Claims & Operations Emerging Sciences team is focused on providing deep insights across the policy & claim lifecycles and making adjuster workflows more efficient by leveraging new technologies and analytical capabilities. The Emerging Sciences team builds and maintains integrated and interactive solutions with a toolkit including generative and agentic AI, natural language processing, and computer vision, as well as more traditional machine learning techniques. We deliver value by partnering closely with the business, IT, and other data science and engineering teams to help build a consistent approach to architecture and practices, while tailoring solutions to our customers' unique needs in accuracy, transparency, and scalability. As a Sr. Data Scientist, you will participate in the entire model lifecycle, partnering with cross-functional business and technical partners to understand business strategies and design, develop, implement, and evolve modeling solutions. We use the latest technologies, machine learning methods, MLOps, and Agile delivery frameworks to build innovative and efficient solutions that maximize business value. This cutting edge and forward focused organization presents the opportunity for collaboration, self-organization within the team, influencing decision-making, and visibility as we focus on continuous business data delivery. This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).
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Job Type
Full-time
Career Level
Mid Level