Data Scientist | Modeling

Machinify, Inc.
La Grange, United States of America
4 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Compensation
$ 230K

Job location

La Grange, United States of America

Tech stack

Artificial Neural Networks
Big Data
Python
Machine Learning
SQL Databases
Data Pipelines
Programming Languages

Job description

We are looking for aData 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.

M Odel Advancement: Curate labeled data, hypothesize new features, and develop ML models that target claims for audit with even greater precision.

Optimize Impact: Measure m odel 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 m odel insights, ensuring alignment with real business needs.

Requirements

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

Benefits & conditions

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 the base salary can range from $180k-$230k+ based on your assessed level. Compensation also includes meaningful equity, healthcare, unlimited PTO, and more.

Equal Employment Opportunity at Machinify

About the company

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.

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