Senior Data Scientist (Level I)

Sun Life•Wellesley, CT

2d•Remote

About The Position

This role will provide advanced analytics expertise within the Business Analytics function that applies the power of data with machine learning to improve business outcomes across the Health and Risk Solutions business. This team is expected to work closely with the other teams across the organization, including functional teams and the analytic agile squads supporting AI use cases within various business domains. This position reports to the Director of Advanced Analytics Transformation within the Health and Risk Solutions business. Responsibilities include development and monitoring of predictive model and AI solutions to support our pricing, underwriting, claim forecasting, and clinical review processes. Additional responsibilities may include applying machine learning techniques to streamline and automate aspects of these processes, mentoring junior team members, and promoting a culture of data-driven risk-based decisions.

Requirements

Strong knowledge of statistical and machine learning framework, with a deep conceptual understanding of time series mythologies and forecasting techniques
Proficiency in Python and SQL for data manipulation, modeling, and automation
Strong problem-solving skills and effective communication, with an ability to explain complex technical concepts to a non-technical audience
Ability to turn proof-of-concept model into production-ready code and reproducible workflows
Commitment to data compliance, model governance and security protocols
Strong business acumen to understand why and how the work we do will impact our business stakeholders
BS/MS in a statistical, mathematical, or technical field (e.g., computer science, actuarial science)
3+ years of experience in developing and implementing data science techniques

Nice To Haves

Experience with actuarial/pricing, underwriting or related concepts utilized in the health insurance field
Demonstrated academic or industry experience with generative AI including prompt engineering, RAG workflows and integrating LLMs into business process
Familiarity with MLOps practices, including model deployment, monitoring, and lifecycle management

Responsibilities

Independently apply data science techniques to solve business problems across various analytical areas, including exploratory data analysis, feature engineering, predictive modeling, and visualization
Develop, validate, and maintain high-quality, robust predictive models and AI solutions that meet business and technical requirements
Utilize multiple sources of data, including structured and unstructured data, along with a variety of machine learning techniques to improve model performance and interpretability
Write clean, modular, and well-documented code that can be deployed and maintained in production, while providing technical guidance and code reviews for junior team members.
Interpret data and model outputs to generate clear actionable insights and recommendations that influence business decisions.