Applied Data Scientist, Healthcare AI

About the company: A fast-growing, profitable enterprise AI company that deploys predictive models into the daily operations of the largest US health systems and health plans. It has fewer than 150 people, is valued above $1B, and lets employees cash out equity through regular secondary sales., The role: Own 3-5 enterprise healthcare accounts end to end. That means designing predictive studies, building production ML pipelines, and turning customer data into insights deployed on the platform. You’ll be the data expert in the room with the clinical, IT, and analytics leaders at major health systems.

• 4+ years as an applied data scientist shipping models to production
• Client-facing experience owning accounts and defending your methodology to skeptical audiences
• Experimental and predictive studies whose results had real commercial impact
• Hands-on distributed data and ML pipelines on Spark/PySpark
• Production Python (pandas, scikit-learn, Airflow). You ship code, not notebooks.
• Healthcare data strongly preferred: claims, EHR, HL7/FHIR, lab data, population health
• Startup or founding-engineer experience a plus

Stack: Python, pandas, scikit-learn, Airflow, Spark/PySpark, SQL, Postgres, AWS (S3, Glue, EMR, MWAA, SageMaker)

Logistics: US work authorization required, no sponsorship · quarterly offsite in NYC · about 10% travel

Keywords: PySpark, Spark, Python, Airflow, AWS, claims, EHR, predictive modeling, client-facing

Remote (US) · Eastern or Central hours · $180K-$240K base (OTE $200K-$260K) + equity

What you’ll do

• Own accounts from data integration and pilot design through production pipelines and ongoing operations
• Design and defend commercially relevant predictive studies, such as predicting which patients will need surgical intervention
• Build automated per-customer pipelines that train, score, and deliver insights at scale
• Run working sessions with client analytics, clinical, and IT teams
• Feed patterns you see across accounts back into the product roadmap