Senior Data Scientist

Sr Data Scientist - GD07AE

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 Senior Data Scientist Employee Benefits Data Science, AI, and Analytics (DSAIA) to develop machine learning and generative artificial intelligence solutions across a range of strategic initiatives.

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.  In addition to our existing portfolio of Predictive AI assets, the team is scaling to expand an end-to-end (E2E), AI-driven reimagination of the EB underwriting and service organizations.  Our E2E projects will include integrated, interactive solutions with a toolkit including generative AI, natural language processing, computer vision, and predictive machine learning techniques.  We deliver value by partnering closely with enterprise enablement and other line of business data science teams to help build a consistent approach to architecture and practices across AI products while tailoring solutions to our customers' unique needs in accuracy, transparency, and scalability.

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.

Responsibilities:

• Create and use 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

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

• Support identifying and assessing the value of new analytical and generative techniques and solution patterns 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 in leveraging generative artificial intelligence (e.g. large language models, image generation, or multimodal generative models) including model selection and tuning, prompt engineering, and performance evaluation

• Experience building modeling solutions in cloud-native environments, such as SageMaker, a plus

• Able to commun