Staff Data Scientist | ML
- Location
- Remote (US)
- Job type
- Full-time
- Level
- Lead / Staff
- Listed
- 30 days ago
Skills & highlights for this role
- SQL
- production machine learning systems
- statistical modeling
- LLM-based workflows
Also useful
- LLM products
Stated benefits & perks
- Medical
- Dental
- Vision
- 401k match
- Unlimited pto
What You'll Do
- 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.
- Innovate on methods and approaches: Go beyond standard analyses by developing new metrics, models, or workflows when existing approaches fall short.
- Build and productionize models: Design, build, and support production ML or LLM-powered solutions in collaboration with engineering and product partners.
- Work deeply with data: Use SQL fluently to explore large datasets, build reliable data assets, and validate results.
- Influence cross-functionally: Partner with Product, Engineering, Finance, and Operations to align on goals, tradeoffs, and execution plans.
- Set technical direction (especially at L7): Raise the bar for data science quality, scalability, and impact across the organization through mentorship, design reviews, and technical leadership.
What You'll Bring
- Proven track record of hands-on data science impact on real-world problems, not just research or dashboards.
- 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.
- Ability to independently scope ambiguous problems, identify the right data and methods, and drive work to completion.
- Strong communication skills, with the ability to explain complex analyses and models to non-technical stakeholders.
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.
About Machinify
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.
Machinify builds machine learning models for some of the largest health plans in the country to identify nearly $1B in erroneous healthcare payments. Our customers receive tens of millions of claims each year, many of which are billed with mistakes or fraud. Our production models detect and stop those errors on a daily basis, resulting in measurable healthcare savings that significantly outperform industry standards. Machinify has already had a huge impact as a small company, and we are growing quickly!
We are looking for a Staff Data Scientist | ML for our "Pay" team (claims payments product) 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.
Equal opportunity and legal notices
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 is an employment at will employer. We participate in E-Verify as required by applicable law.
In accordance with applicable state laws, we do not inquire about salary history during the recruitment process. If you require a reasonable accommodation to complete any part of the application or recruitment process, please let our recruiters know.
From Machinify's careers page, first listed on Rolev.ai on October 10, 2026. Rolev.ai is not the employer.
How Rolev.ai helps with this application
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Common questions
Where is the Staff Data Scientist | ML job at Machinify located?
Yes, this Staff Data Scientist | ML opening at Machinify is listed as Remote (US). Always check Machinify's application page for specific state eligibility or relocation policies.
What qualifications are required for Staff Data Scientist | ML at Machinify?
For the Staff Data Scientist | ML role, Machinify highlights experience with SQL, production machine learning systems, statistical modeling, LLM-based workflows. Rolev.ai can score your existing resume against these requirements and show any skill gaps before you apply.
How do I apply for the Staff Data Scientist | ML position at Machinify?
This listing links directly to Machinify's Greenhouse application page. You can apply directly on Machinify's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Greenhouse application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.