Responsibilities

Decipher and automate complex, branching workflows for insurance coverage, affordability programs, and fulfillment
Combine AI/ML approaches for high-precision document classification, unstructured data extraction, and reference-based question answering
Automate multi-step, path-dependent processes using RPA/scraping approaches to navigate third-party platforms
Build state machines that drive system decisions and handle failure modes across intertwined processes
Scale data and ML pipelines across growing drug classes, patient populations, and provider markets
Make data and ML pipelines robust to variation in input data formats (clinical documentation)
Leverage empirical data to build understanding of opaque external systems (insurance policies)
Create consumer-grade experiences for patients and physicians incorporating AI-powered workflows
Translate heterogeneous data into reliable insights informing clinical indication selection, launch markets, and insurer negotiations
Develop predictive and simulation models to forecast clinical trial site performance, drug adoption rates, and rebate/subsidy impacts
Use real-time data and engagement channels to enroll criteria-matching patients and physicians in clinical studies and access programs

Requirements

Strong programming skills and general Computer Science knowledge
Product engineering experience building web apps full-stack, integrating with APIs and services, iterating on what to build
Track record of moving quickly, finding shortcuts, and going to unreasonable lengths to deliver on goals