Responsibilities
- Utilize advanced analytics methods to process large volumes of structured and unstructured data for actionable business insights.
- Lead full lifecycle development of predictive models aimed at high-impact outcomes such as claims efficiency improvements.
- Design and test hypotheses using statistically sound methodologies to support model development.
- Construct machine learning systems that integrate structured data, textual inputs, and generative AI technologies.
- Select suitable algorithms and validation approaches to ensure models deliver measurable performance and business impact.
- Ensure training datasets are properly assembled and labeled, with engineered features to support model accuracy.
- Implement production-grade code following ML Ops standards, ensuring reproducibility and organized code repositories.
- Remain current with advancements in data science and machine learning to address complex challenges in insurance claims.
- Guide and mentor junior data science team members in technical approaches and best practices.
- Take ownership of major project components involving moderate to high complexity.
- Present technical findings clearly through reports, presentations, and recommendations to both technical and non-technical audiences.
- Collaborate with cross-functional teams and contribute to organizational data science communities to advance best practices.
Work Arrangement
Hybrid — Boston, MA, Portsmouth, NH, Seattle, WA, Columbus, OH, Plano, TX
Other
- Candidates residing within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX are expected to follow a hybrid work model with two office days per week.
- Candidates outside these regions will work remotely with periodic travel requirements.
