Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.
We are seeking a Staff Data Scientist. This role will be responsible for leading the build and deployment of classical ML and GenAI systems that generate and validate billing error, audit, and fraud concepts in healthcare payments. You will partner with SMEs and cross-functional stakeholders to deliver production-grade capabilities with clear customer and financial impact.
What you will do:
Build and ship classical ML and GenAI pipelines that surface, rank, and explain candidate billing error, audit, and fraud concepts across large-scale healthcare claims data
Advance state-of-the-art research in anomaly detection, information retrieval, and weak supervision, turning research into robust production systems
Accelerate concept generation and concept validation with SMEs by designing human-in-the-loop workflows that enable high-throughput review with strong precision, recall, and traceability
Own end-to-end delivery from problem framing and data strategy to model development, evaluation, deployment, and monitoring, with a focus on reliability and customer outcomes
Partner cross-functionally with Product, Data Engineering, Operations, SMEs, and business leaders to prioritize work, unblock execution, and unlock material financial value
Help scale the team and platform as Machinify grows aggressively toward resale and IPO readiness
What you bring:
Degree in Computer Science, Engineering, or a related field
5+ years of professional data science experience
Proven track record of managing data teams and delivering complex, high-impact products from concept to deployment
Strong knowledge of data privacy regulations and best practices in data securit