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 

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.