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 within Personal Lines to develop machine learning and artificial intelligence solutions across a range of strategic initiatives.

Sales & Underwriting Analytics

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.

Must be eligible to work in the US without company sponsorship

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 the vision and sustains a culture that treats analytics as a corporate asset
• Lead execution of modeling and machine learning projects that focus on internal team collaboration with Data Scientists, Data Engineers, and Performance Analysts
• 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 applicable to your function
• 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 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

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

Equal Opportunity Employer/Females/Minorities/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

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