
Director of Data Science, Employee Benefits Data Science (Pricing & Rating)
- Hartford, CT
- Permanent
- Full-time
- Manage a team of Data Scientists to develop, test, validate, and maintain a portfolio of rating models for the Employee Benefits class plans in Long-Term Disability, Short-Term Disability, and Life
- Continuously 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 cross-functional projects that include the creation of statistical models and machine learning techniques to achieve financial objectives, solve business problems, and identify long-term opportunities that enhance actuarial modeling.
- Collaborate and partner with business stakeholders in a way that supports the vision and sustains a culture that treats analytics as a corporate asset.
- Advance the department’s capabilities by creating and deploying long-term tools to continually evolve the practice of data science, with an ability to see the end-to-end solution.
- Develop strategies to achieve targeted business objectives. Implement these strategies and follow through to successful conclusion.
- Remain current on research techniques and become familiar with state-of-the-art tools applicable to your function.
- Participate in the talent management process for hiring, onboarding, training and development of staff.
- Collaborate with your leader to provide timely feedback on development and opportunities for your team.
- Learn/bring best practices to guide the direction of our Data Science and Data Engineering workflows.
- 8+ 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
- Expertise in actuarial modeling; experience in Employee Benefits pricing is a plus.
- Experience with managing Data Scientists and providing guidance through model development
- Expertise in statistical modeling, inference, and building machine learning algorithms in Python
- Expertise in SQL and navigating databases to extract relevant attributes
- Expertise in Unix and Git
- Expertise 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 technique
- 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.