About In-House Health

In House Health is a seed-stage startup that aims to help the global healthcare crisis marked by severe staffing shortages and rising nurse burnout. In-House Health develops a cutting-edge platform that empowers nursing teams to self-manage with unprecedented flexibility. Our innovative solution leverages a predictive AI algorithm to optimize nurse shift assignments.

In-House Health is backed by one of the largest VCs in the world, and the CEO is Ari Brenner, who previously founded a digital health company with a >$1B valuation.

About the role

We're looking for a Data Science Team Lead to found and lead our new data science team. You'll report directly to our VP R&D. For the first few months you'll be our first and only data scientist, building the prediction engine behind our staffing platform hands-on. Then you'll hire and grow a team under you.

• Census prediction: forecast how many patients each hospital unit will have, shift by shift, from hours to weeks ahead, using admissions, discharges, transfers, seasonality and hospital-specific patterns.
• Patient acuity: model how sick patients are and how much nursing care they need, based on clinical data such as vitals, diagnoses, orders and nursing assessments.
• Nursing workload: combine census and acuity into workload predictions that drive how nurse shifts are planned and assigned.
• Clinical data: work hands-on with EHR records and ADT feeds from hospitals: messy, incomplete and full of signal.
• Own models end to end, from exploration and validation to production and monitoring.
• Build the team: define the DS roadmap, hire your first data scientists, and set the team's standards and practices.

About you

• You are highly motivated and excited to work at an early-stage company
• 7+ years as a data scientist, with models running in production
• Experience leading or mentoring data scientists, or a strong desire to build and lead a team
• Comfortable being hands-on as a solo DS before the team grows
• Strong hands-on experience with time-series forecasting and classification models
• Strong Python and SQL
• An advantage is given to those with experience in healthcare or clinical data (EHR, ADT, HL7/FHIR)