The Hartford seeks to develop machine learning and artificial intelligence solutions across a range of strategic initiatives within 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
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
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
The company will not support the STEM OPT I-983 Training Plan endorsement for this position
