Director of Data Science, Actuarial Modeling

Responsibilities: 

• 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. 

Qualifications: 

• 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 mentoring 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 techniques 

This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).

Candidates 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.

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.