Staff Data Scientist | Fraud / Billing
MachinifyRemote - USPosted 21 March 2026
Job Description
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 security
Exceptional team management abilities, with experience in building and leading high-performing teams
Ability to think strategically and execute methodically
Ability to drive change and inspire a distributed team
Strong problem-solving skills and a data-driven mindset
Ability to communicate effectively, collaborating with diverse groups to solve complex problems
You embrace challenges with optimism and a balanced, and bring a business-oriented approach to product development
You prioritize customer impact, ensuring your work significantly advances our product value
What We Offer
Work from anywhere in the US! Machinify is digital-first.
Top Medical/Dental/Vision offerings
FSA/HSA
Tuition reimbursement
Competitive salary, 401(k) with company match
Unlimited PTO
Additional health and wellness benefits and perks
Flexible and trusting environment where you’ll feel empowered to do your best work
The salary for this position is based on an array of factors unique to each candidate: Such as years and depth of experience, set skills, certifications, etc. We are hiring for different levels, and our Recruiting team will let you know if you qualify for a different role/range. Salary is one component of the total compensation package, which includes meaningful equity, excellent healthcare, flexible time off, and other benefits and perks.
Equal Employment Opportunity at Machinify
We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace. Machinify ... (truncated, view full listing at source)
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