Staff Data Scientist | Modeling
MachinifyRemote - USPosted 21 March 2026
Tech Stack
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.
Our team builds machine learning models for some of the largest health plans in the country to identify and audit claims errors from simple errors to outright fraud. Large insurance payers receive tens of millions of claims each year, of which they only are able to audit less than 10%. Our claim selection models help with selecting the right set of claims to audit and recover hundreds of millions of dollars each year reducing wasted medical spend. We have made significant progress already, with models that significantly outperform even selection by human experts but there are still many improvements to be made.
We are looking for a Staff Data Scientist to advance our models further. In addition to building best-in-class models, this person will create technical frameworks and tools to help the team achieve greater scale and improved business outcomes.
What You’ll Do:
Master Claim Audits: Dive into various clinical and coding audits, unpacking the data that drives audit decisions, and understanding past outcomes.
Model Advancement: Curate labeled data, hypothesize new features, and develop ML models that target claims for audit with even greater precision.
Optimize Impact: Measure model performance rigorously, translating outcomes into operational insights and recommendations for our clients.
Tame Complexity: Work with vast, nuanced data pipelines derived from intricate business workflows—often unclean and undefined—to refine them for robust modeling.
Drive Team Success: Enhance data pipelines, modeling infrastructure, and team tools to level up our capabilities and decision-making efficiency.
Grow with Healthcare: Build expertise in healthcare data and make an impact on the industry. Engage with clients and internal experts to refine model insights, ensuring alignment with real business needs.
What You Bring:
You enjoy solving real-world business problems involving data-driven optimization and ML modeling - and have been doing that successfully for a while.
You are comfortable measuring and optimizing the direct business impact of work.
You are experienced with SQL, handling large-scale data, and are comfortable with at least one programming language (Python, R, etc.).
You have experience building ML models using modern ML approaches like Neural Nets or Tree-ensembles from scratch for new applications - making decisions relating to which supervised labels to use, the metric to optimize for, and the features likely to be useful
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, ... (truncated, view full listing at source)
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