Lead Data Scientist
Skills
About the Role
As a Lead Data Scientist you will drive high-impact analytics and modeling initiatives that directly inform product development, clinical strategy, and business decision-making. You'll operate as a senior individual contributor, owning complex problem spaces end-to-end, from framing ambiguous questions to delivering actionable insights and scalable solutions. You'll partner closely with Product, Clinical, and Engineering teams to translate real-world healthcare challenges into data-driven strategies, while helping elevate analytical rigor and best practices across the team. This role is ideal for someone who thrives in a fast-paced, mission-driven environment and wants to make a meaningful impact on how care is delivered.
Requirements
- 6+ years of experience in data science, ideally in healthcare or a mission-driven environment
- Strong experience translating ambiguous problems into structured analytical solutions
- Expertise in statistical modeling, experimentation, and data analysis methods
- Hands-on experience tuning and evaluating ML models for NLP tasks (e.g., transformers, large language models)
- Experience building and scaling data science solutions in cloud environments (e.g., AWS)
- Expertise in Python programming language
- Experience working with large, complex datasets (e.g., claims, EMR, or similar)
- Ability to communicate complex insights to both technical and non-technical audiences
- Experience applying data science solutions on healthcare data (EMR, claims, SDOH data)
- Familiarity with distributed data processing (e.g., PySpark, Spark SQL)
- Experience with search or graph-based data systems (e.g., Elasticsearch, graph databases)
Responsibilities
- Own and execute end-to-end analytical projects, from problem framing and data exploration to model development and delivery of insights
- Partner with cross-functional stakeholders to translate business and clinical questions into structured analytical approaches
- Develop advanced statistical models and analytical frameworks to drive decision-making and optimize outcomes
- Design and implement experiments, performance tracking, and hypothesis-driven analyses to inform product and clinical strategies
- Build scalable, reusable code and analytical assets to support ongoing insights and operationalization
- Communicate findings clearly through data visualization, storytelling, and executive-ready presentations
- Contribute to team best practices and mentor junior team members through guidance and review