HealthLeap AI
HealthLeap builds AI that helps clinicians prioritize patients, surface the right data, and get patients the care they need earlier so they can leave the hospital sooner. We integrate with hospital electronic health record systems, screen 100% of patients daily, and risk‑rank them in real time. Clinicians at Cedars‑Sinai and Penn Medicine start every morning with HealthLeap — with Houston Methodist, Emory, and Intermountain Health deploying now. We started with malnutrition and are expanding to every major condition to ensure no patient falls through the cracks. Sequoia and First Round back us as we build the platform that screens every patient for everything and drives tangible outcomes.
Real results : 39% more diagnoses, 4 days earlier detection, $11M/year ROI for our first site at Cedars‑Sinai, 7× revenue growth in 7 months.
Outcomes You’ll Drive Condition expansion velocity : Idea → signal & label viability using current EHR data → validated model → customer‑ready (weeks, not months).
Improving patient health outcomes : Quantified length of stay (LOS) reduction, readmission reduction, mortality reduction, with clear confidence intervals and robust counter‑factuals.
Pilot → production conversion : Run retrospective analyses on hospital data to prove impact, then transition validated pilots into live deployments that deliver measurable outcomes.
Role Overview We’re looking to hire a product‑mindful Data Scientist with a solid theoretical foundation. 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, 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.
Key Responsibilities
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.
Requirements
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.
Nice‑to‑Haves
Background in applied AI companies with strong product traction (not hype‑driven firms).
Interest in healthcare data (e.g., from research labs with practical applications).
Side projects demonstrating productionization (e.g., turning prototypes like landing agents into reliable systems).
Uncertainty quantification.
Covariate and prediction drift detection in production.
Hands‑on experience with LLMs in production; LLMs for clinical text, weak/active/semi‑supervised learning.
Strong software engineering skills with proven ML experience: productionizing models (tabular/text data preferred; not pure vision specialists) and building scalable pipelines.
Familiarity with EHR schemas/standards (FHIR/HL7), IRB/validation study workflows, and model governance.
We Provide
Competitive salary with performance‑based incentives.
Comprehensive Healthcare Benefits – we cover 100% of premiums for employees.
Unlimited Paid Time Off – we want you at your best. Our recommended 20 PTO days per year let you schedule your work around your life.
401K match of up to 4% of employee salary.
Laptop and equipment budget to set up your at‑home office environment.
Lunch, snacks, and drinks are provided in the office to ensure you never go hungry.
Opportunity for professional growth in a dynamic, fast‑paced startup environment.
Location: San Francisco (hybrid). Compensation is dependent on experience, overall fit to our role, and candidate location.
If you’re passionate about applying frontier AI to real‑world impact, join us in building healthcare’s future.
#J-18808-Ljbffr
Real results : 39% more diagnoses, 4 days earlier detection, $11M/year ROI for our first site at Cedars‑Sinai, 7× revenue growth in 7 months.
Outcomes You’ll Drive Condition expansion velocity : Idea → signal & label viability using current EHR data → validated model → customer‑ready (weeks, not months).
Improving patient health outcomes : Quantified length of stay (LOS) reduction, readmission reduction, mortality reduction, with clear confidence intervals and robust counter‑factuals.
Pilot → production conversion : Run retrospective analyses on hospital data to prove impact, then transition validated pilots into live deployments that deliver measurable outcomes.
Role Overview We’re looking to hire a product‑mindful Data Scientist with a solid theoretical foundation. 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, 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.
Key Responsibilities
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.
Requirements
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.
Nice‑to‑Haves
Background in applied AI companies with strong product traction (not hype‑driven firms).
Interest in healthcare data (e.g., from research labs with practical applications).
Side projects demonstrating productionization (e.g., turning prototypes like landing agents into reliable systems).
Uncertainty quantification.
Covariate and prediction drift detection in production.
Hands‑on experience with LLMs in production; LLMs for clinical text, weak/active/semi‑supervised learning.
Strong software engineering skills with proven ML experience: productionizing models (tabular/text data preferred; not pure vision specialists) and building scalable pipelines.
Familiarity with EHR schemas/standards (FHIR/HL7), IRB/validation study workflows, and model governance.
We Provide
Competitive salary with performance‑based incentives.
Comprehensive Healthcare Benefits – we cover 100% of premiums for employees.
Unlimited Paid Time Off – we want you at your best. Our recommended 20 PTO days per year let you schedule your work around your life.
401K match of up to 4% of employee salary.
Laptop and equipment budget to set up your at‑home office environment.
Lunch, snacks, and drinks are provided in the office to ensure you never go hungry.
Opportunity for professional growth in a dynamic, fast‑paced startup environment.
Location: San Francisco (hybrid). Compensation is dependent on experience, overall fit to our role, and candidate location.
If you’re passionate about applying frontier AI to real‑world impact, join us in building healthcare’s future.
#J-18808-Ljbffr