At Stellar Health, we help primary care providers put patient health first. Our platform - a mix of technology, people, and analytics - supports providers at the point of care, delivering real-time patient information, activating practice staff, and empowering providers and care teams with incentives that reward the work they are already doing to keep patients healthy. The central question you will answer is: "How can we predictably change provider behavior to drive better patient outcomes?"
Requirements
- Deep expertise in causal inference (e.g., A/B testing, quasi-experimental designs, instrumental variables), statistical analysis, predictive modeling (e.g., classification, recommenders), and machine learning techniques.
- Strong proficiency in Python (preferred) or R and SQL is required.
- Significant experience in product analytics, specifically the interpretation of user behavior from event-level data logs and collaborating with engineers to tie the outputs of models to automated decision-making systems.
- Experience or strong understanding of advanced experimentation frameworks, including multi-armed bandit testing and reinforcement learning applications is a strong plus.
- Experience or a strong understanding of MLOps principles and the lifecycle of deploying and maintaining models in production environments is a plus.
- Familiarity with healthcare data is a plus.
Responsibilities
- Design and Execute Rigorous Experiments: Lead our scientific approach to experimentation, from hypothesis generation and statistical design to the analysis and interpretation of results.
- Develop and Implement Predictive Models: Design and build models to understand and influence provider behavior, such as intervention recommenders and user segmentation, and partner with engineering to tie model outputs to automated systems.
- Scale Our Experimentation System & Culture: Partner with co-leads to mature our experimentation capabilities, guiding the program from its early phases to a scaled, high-impact function.
- Translate Data into Actionable Insights: Convert complex analytical findings, often stemming from ad-hoc deep-dive investigations into critical business problems and hidden opportunities, into clear, compelling, and actionable insights; present these findings to business stakeholders, including our leadership team, and various departments to drive strategy and operational improvements.
- Collaborate on Data Strategy & Infrastructure: Partner with the Lead Data Scientist, analysts, and the analytics engineering team to contribute to our shared data assets, shape the data science technology stack, and guide decisions on analytical tools, data governance, experimentation governance, and quality.
- Drive a Culture of Knowledge Sharing: Actively share insights, methodologies, and findings with the Lead Data Scientist and other relevant teams to foster a collaborative environment, prevent duplicated efforts, and maximize the impact of our data.
Other
- A Master’s degree or PhD in a quantitative field (e.g., Statistics, Computer Science, Economics) or equivalent practical experience.
- Extensive experience (typically 8-10+ years) as a Data Scientist delivering impactful business insights and predictive models, including demonstrated experience in a senior or lead capacity guiding complex projects and mentoring team members.
- Exceptional ability to translate complex business problems into analytical frameworks and convert sophisticated findings into clear, actionable strategic recommendations for both technical and non-technical audiences.
- A strategic mindset with proven experience collaborating effectively with cross-functional teams (e.g., product, engineering, analysts, operations) to understand broad business objectives, identify high-impact data science opportunities, and contribute to the overall data and analytics roadmap.
- You'll love this job if you are a builder who thrives on defining strategy, not just executing on it.
