Founding Data Scientist, HealthLeap

• Passionate about AI's potential in healthcare; outcomes-oriented with a focus on impact, not just research
• Statistics: parametric and non-parametric tests, hypothesis testing, experimental design, confidence intervals, and causal inference basics
• ML fluency: Python, SQL; polars (or pandas), scikit-learn, XGBoost/LightGBM (PyTorch/transformers a plus); survival/time-to-event experience is great
• Visualization & storytelling: Expert at turning complex analyses into crisp user visualizations, dashboards, and narratives for clinicians and executives
• Customer-facing: Comfortable interviewing stakeholders, presenting to AI/data science leaders, and defending methods
• Read the latest research and rapidly translate new statistical/ML papers into pragmatic wins
• 3 - 5+ years of relevant experience from a high-growth environment
• BS/MS in Statistics, Biostats, CS, or equivalent experience
• Resourceful, fast learner, high ownership, bias to action, fast experimentation cycles, and ability to work independently while collaborating in a small team
• Understanding of fairness: Independence, Separation, and Sufficiency
• We’re looking to hire a product-minded Data Scientist with a sound theoretical knowledge foundation
• If you're passionate about applying frontier AI to real-world impact, join us in building healthcare's future

Desirable

• You will own end-to-end problem framing, timeline scoping, experimental design, and model iteration
• You'll work closely with our CEO and small team to launch new models quickly and safely by leveraging and expanding on our existing feature tables
• You will also run retrospective pilots to estimate clinical and financial impact (reimbursement lift, LOS reduction, mortality reduction) and support pre-sales by meeting AI/Data Science leaders at world-class health systems to share your clinical and financial model assumptions and development methodologies
• Own end-to-end modeling from financial incentives and problem framing to a validated model
• Estimate impact with rigorous retrospective analyses (LOS, readmissions, mortality, reimbursement)
• Productionize pipelines and rollouts with reliability
• Monitor & improve: drift, calibration/uncertainty, and fairness (Independence/Separation/Sufficiency)
• Translate research into pragmatic wins for our platform
• Partner with stakeholders: clear visuals, crisp narratives, and method presentation for analysts, clinicians, and executives
• Outcomes You’ll Drive:
• Condition expansion velocity: Idea → signal & label viability using current EHR data → validated model → customer-ready (for viable use cases, weeks, not months)
• Improving patient health outcomes: Quantified length of stay (LOS) reduction, readmission reduction, mortality reduction, with clear confidence intervals and robust counterfactuals
• Pilot → production conversion: Run retrospective analyses on hospital data to prove impact, then transition validated pilots into live deployments that deliver measurable outcomes