The ideal Senior Data Scientist delivers production-quality solutions for insurance business problems using foundation models and data science fundamentals. They take initiative, own their work from problem definition through deployment, and proactively communicate progress, blockers, and considerations with the team and leadership. They excel at systems thinking-decomposing complex problems, designing measurement frameworks, and optimizing across multiple dimensions. They work in Azure/Databricks using Python with git version control and produce maintainable code with strong documentation.
Responsibilities:
- Collaborate with scientists, engineers, and technical product owners to create technical solutions · Deliver production solutions for data extraction, classification, routing, search, and decision-making using foundation models
- Manipulate and analyze data programmatically, derive statistically sound insights, and communicate findings that address technical and business considerations
- Engineer and evaluate foundation model prompts systematically across domain datasets
- Design comprehensive evaluation frameworks using precision, recall, F-1 scores, accuracy, and operational metrics
- Own documentation and mentor junior team members through systematic problem-solving approaches
- Decompose complex problems into measurable components, optimize across multiple dimensions, and articulate trade-offs
- Strong collaboration skills, capable of conveying statistical performance and business considerations and proactively surface issues to keep technical personas informed
- Strong Python proficiency from a functional programming paradigm, including dependency management, virtual environments, and git version control following git flow practices
- Proven experience with cloud platforms like Azure and Databricks using foundation model APIs (OpenAI, Anthropic, Google, etc.)
- Deep familiarity with ML fundamentals (supervised/unsupervised learning, evaluation metrics, model validation) and statistical methods
- Experience designing and implementing solutions with foundation models, including prompt engineering and output validation
- Proven capability to deliver solutions from inception through production with variable autonomy, iteratively refining through diagnosis, hypothesis testing, and systematic improvement
- 5-15 years of relevant professional experience in data science, machine learning, or related fields; advanced graduate research or academic work may substitute for professional experience
- Graduate degree in quantitative field with strong systems thinking emphasis (Computer Science, Statistics, Economics, Physics, Mathematics, Operations Research, Computational Linguistics) · Insurance industry experience with document processing
- Experience with agent frameworks (LangChain, LlamaIndex), multi-agent orchestration, RAG systems, or vector databases
- Experience with production ML monitoring, experimental design, self-healing systems, or automated optimization
