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 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

Director of Data Science, Actuarial Modeling

Key skills