Director of Data Science, Actuarial Modeling

The Hartford•Boston, MA

22h•Hybrid

About The Position

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. The Hartford seeks a Director & Data Scientist within Employee Benefits to develop statistical and machine learning solutions supporting actuarial pricing and reserving modeling. In this role, you will be a hands-on technical expert contributing across the full model lifecycle—partnering closely with actuarial, business, and engineering stakeholders to understand business strategies and translate them into robust, scalable modeling solutions. You will design, develop, implement, and evolve advanced analytics and machine learning models using modern technologies, MLOps practices, and Agile delivery frameworks. This cutting-edge, forward-focused organization offers the opportunity to work autonomously on high-impact problems, influence technical and analytical decisions, collaborate deeply with cross‑functional partners, and gain strong visibility as we focus on continuous, value-driven data and model delivery. 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).

Requirements

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
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.

Nice To Haves

Experience building modeling solutions in cloud-native environments, such as Sagemaker, a plus

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.