The Hartford seeks to develop machine learning and artificial intelligence solutions across a range of strategic initiatives in Personal Insurance
Requirements
- 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
- 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
- 5+ years of relevant experience recommended
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
- 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
- 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
Other
- 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
- 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
