data
Posted 2 weeks agoStaff Data Scientist | ML
at Machinify
United StatesRemote
Responsibilities
- Solve real business problems with data: Own end-to-end data science work, from problem framing and data exploration to modeling, validation, and production impact. •
- Lead high-impact initiatives: Drive complex efforts such as vendor leakage detection and prevention, delivering measurable revenue or cost improvements. •
- Build and productionize models: Design, build, and support production ML or LLM-powered solutions in collaboration with engineering and product partners. •
- Influence cross-functionally: Partner with Product, Engineering, Finance, and Operations to align on goals, tradeoffs, and execution plans. •
- Proven track record of hands-on data science impact on real-world problems, not just research or dashboards. •
Requirements
- 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 are looking for a Staff Data Scientist | ML for our "Pay" team (claims payments product) to advance our models further.
- Work deeply with data: Use SQL fluently to explore large datasets, build reliable data assets, and validate results. •
- Strong proficiency in SQL, with
- experience working directly on complex, large-scale datasets. •
- Experience building, shipping, or supporting production ML systems, preferably some exposure to LLM-based products or workflows. •
- Strong communication skills, with the ability to explain complex analyses and models to non-technical stakeholders.
Benefits
- 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.
- Machinify builds machine learning models for some of the largest health plans in the country to identify nearly $1B in erroneous healthcare payments.
- Our production models detect and stop those errors on a daily basis, resulting in measurable healthcare savings that significantly outperform industry standards.