About the role
We are seeking a Healthcare Data Scientist (Real-World Evidence) to answer complex healthcare and biopharma questions using large-scale real-world data and help shape the next generation of AI-powered clinical research. This is a hands-on role that combines rigorous real-world evidence analysis with deep collaboration across product, AI, and customer teams. Internally, you will partner closely with Medeloop's AI research and product teams to design and execute real-world evidence analyses, evaluate the performance of our clinical research agents, and apply your scientific expertise to improve how our platform reasons about healthcare data. Externally, you will serve as an embedded data scientist for our partner institutions, working directly with clinicians, researchers, and life sciences organizations to scope research questions, deliver high-quality evidence, drive adoption of the platform, and expand each customer's use of Medeloop over time. This is a highly technical role centered on analytical reasoning, statistical rigor, and scientific problem-solving. Your work will directly influence both the intelligence of our AI systems and the real-world impact they create for healthcare and life sciences organizations. Role &
Responsibilities Build and execute real-world evidence analyses to answer complex healthcare and biopharma questions using large-scale claims and EHR data. Write high-quality, scalable code (SQL and Python; R also welcome) to define cohorts, model patient journeys, and generate rigorous, reproducible research outputs. Apply clinical and statistical judgment to evaluate treatment patterns, utilization, and outcomes in observational data, reasoning carefully about bias and limitations. Work directly with Medeloop's AI research agents, reviewing and challenging their analytical outputs, and partner with AI and product teams to improve how agents translate research questions into high-quality analyses. Partner with customer institutions: scope their research questions, deliver evidence, drive adoption, and help expand how each institution uses Medeloop. Communicate analyses, assumptions, and limitations clearly to internal teams, scientific stakeholders, and customers, translating technical findings into plain-language insight. Help shape the analytical foundations and evaluation frameworks behind the next generation of AI-driven clinical research tools.
