Machinify

healthcare intelligence

StaffDataScientist|ML

United States Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Staff candidates.

The Brief

“Staff Data Scientist | ML at Machinify. Skills: end-to-end data science work, building and productionizing models, technical frameworks and tools. Own end-to-end data science work, from problem framing and data exploration to modeling, validation, and production impact. Lead high-impact initiatives”

What You'll Achieve.

deliver measurable revenue or cost improvements; achieve greater scale and improved business outcomes; significantly outperform industry standards

Industry & Context.

healthcare intelligence
Problems you'll solve

Solve real business problems with data; independently scope ambiguous problems; identify the right data and methods

What They're Looking For.

Must Have

SQL proficiency, experience working directly on complex, large-scale datasets, Experience building, shipping, or supporting production ML systems, Ability to independently scope ambiguous problems, identify the right data and methods, and drive work to completion, communication skills

Nice to Have

some exposure to LLM-based products or workflows

What You'll Do.

Own end-to-end data science work

from problem framing and data exploration to modeling

and production impact

Lead high-impact initiatives

Innovate on methods and approaches

Build and productionize models

Work deeply with data

Influence cross-functionally

Set technical direction

Raise the bar for data science quality

and impact across the organization through mentorship

and technical leadership

How You'll Work.

Team & Collaboration

Partner with Product, Engineering, Finance, and Operations to align on goals, tradeoffs, and execution plans; collaboration with engineering and product partners

Communication Scope

communication skills; ability to explain complex analyses and models to non-technical stakeholders

Process & Methodology

independently scope ambiguous problems, drive work to completion

Full 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. 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. 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 colla

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