Senior Data Scientist

The Hartford seeks a Senior Data Scientist Employee Benefits Data Science, AI, and Analytics (DSAIA) to develop machine learning solutions supporting actuarial pricing, underwriting, and reserving modeling. 

The Employee Benefits DSAIA team is a rapidly growing team focused on providing deep insight, automation, and augmentation across the policy lifecycle for Employee Benefits customers and internal business stakeholders.  The EB DSAIA team supports a portfolio across the EB lifecycle, from sales and underwriting to policy installation, renewal, and everything in between.   

As a Senior Data Scientist in the Employee Benefits DSAIA team, you will participate in the entire solution lifecycle. You’ll partner with cross-functional business and technical partners to understand business strategies and design, develop, implement, and evolve modeling solutions. We use the latest generative models, machine learning methods, MLOps deployment methods, 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 value 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).

Responsibilities: 

• Create statistical models, algorithms, and machine learning techniques 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 and sustains a culture that treats analytics as a corporate asset 

• Partner with Actuarial and Data teams to monitor and manage the End-to-End lifecycle of the rating models and underlying data which feeds them  

• Lead execution of modeling and machine learning projects that focus on internal team collaboration with Data Scientists, Data Engineers, and Product Owners  

• 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 become familiar with state-of-the-art tools in generative AI 

• Provide economic, qualitative, and statistical support to ensure accuracy of characteristics and metrics being applied to business decisions 

• Learn/bring best practices to guide the direction of our Data Science and Data Engineering workflows  

Qualifications: 

• 5+ years of relevant industry experience recommended 

• Master’s or Ph.D. in Statistics, Applied Mathematics, Quantitative Economics, Actuarial Science, Data Science, Computer Science, or a similar analytical field; or progress towards a relevant professional designation  

• Proficiency in statistical modeling, inference, and building machine learning algorithms in Python  

• Proficiency in SQL and navigating databases to extract relevant attributes  

• Proficiency in Unix and Git  

• Proficiency in the end-to-end modeling lifecycle, from requirements gathering to monitoring and validation 

• Experience building modeling solutions in cloud-native environments, such as SageMaker, 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. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.