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Remote Core Measures Data Abstractor Jobs in Missouri

Team members who choose virtual / remote work should have an adequate space to serve as their home ... Central Standard Time core business hours . This position will require employees to come on site to ...

Team members who choose virtual / remote work should have an adequate space to serve as their home ... Central Standard Time core business hours . This position will require employees to come on site to ...

Team members who choose virtual / remote work should have an adequate space to serve as their home ... Central Standard Time core business hours This position will require employees to come on site to ...

Fri remote) for candidates in the Kansas City area and open to qualified remote candidates outside ... Develop and monitor key performance indicators (KPIs) to measure product success and impact.

Data Engineer - Multiple Positions

Chesterfield, MO · Remote

$113K - $136K/yr

United States - Remote Employment Type: Full-Time and Contract Data Engineer Description: As a Data ... In-depth knowledge of data architecture, including Spark Streaming, Spark Core, Spark SQL, and data ...

You will operate in an international, remote environment where clear communication, accountability ... Support the development and monitoring of KPIs to measure delivery progress, team performance, and ...

$90K - $110K/yr

This is a fully remote opportunity for an experienced product leader to own and grow a crypto ... This processing is based on legitimate interest and pre-contractual measures under applicable data ...

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Remote Core Measures Data Abstractor information

See Missouri salary details

$13

$23

$36

How much do remote core measures data abstractor jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for remote core measures data abstractor in Missouri is $23.76, according to ZipRecruiter salary data. Most workers in this role earn between $17.36 and $30.00 per hour, depending on experience, location, and employer.

What is a remote core measures data abstractor?

A Remote Core Measures Data Abstractor is responsible for reviewing and analyzing medical records to ensure compliance with core quality measures set by regulatory agencies like CMS and The Joint Commission. They work remotely to extract data, identify trends, and report findings that help improve patient outcomes and hospital performance. This role requires clinical knowledge, attention to detail, and experience with electronic health records (EHR) and quality reporting systems.

What are the key skills and qualifications needed to thrive as a remote core measures data abstractor?

To thrive as a Remote Core Measures Data Abstractor, you need in-depth knowledge of medical terminology, healthcare standards, and experience in data abstraction or chart review, often supported by a healthcare degree or clinical background. Familiarity with electronic health records (EHRs), core measure abstraction software, and certifications such as Certified Health Data Analyst (CHDA) are highly valued. Strong attention to detail, time management, and effective written communication are essential soft skills for this detail-oriented and remote-based role. These competencies ensure the accurate extraction and reporting of clinical data, supporting hospital compliance and quality improvement initiatives.

What are some of the common challenges faced by remote core measures data abstractors?

Remote Core Measures Data Abstractors often face challenges related to interpreting diverse clinical documentation across multiple healthcare providers and ensuring complete, accurate data abstraction in accordance with ever-evolving regulatory requirements. Staying organized and managing time effectively is essential when working remotely to meet tight submission deadlines. Additionally, collaborating virtually with clinical teams and quality departments requires strong communication skills and proactive follow-up. Overcoming these challenges not only ensures compliance but also contributes to the overall quality improvement efforts of the organization.

What are popular job titles related to Remote Core Measures Data Abstractor jobs in Missouri?

For Remote Core Measures Data Abstractor jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Remote Core Measures Data Abstractor jobs in Missouri look for?

The top searched job categories for Remote Core Measures Data Abstractor jobs in Missouri are:

What cities in Missouri are hiring for Remote Core Measures Data Abstractor jobs?

Cities in Missouri with the most Remote Core Measures Data Abstractor job openings:

Infographic showing various Remote Core Measures Data Abstractor job openings in Missouri as of July 2026, with employment types broken down into 3% Internship, 79% Full Time, 7% Part Time, and 11% Contract. Highlights an 100% Remote job distribution, with an average salary of $49,421 per year, or $23.8 per hour.

Full-time

Re-posted 27 days ago


Enterprise Holdings rating

7.1

Company rating: 7.1 out of 10

Based on 268 frontline employees who took The Breakroom Quiz

117th of 177 rated vehicle equipment hire


Job description

ABOUT THE COMPANY

Enterprise Mobility is a leading provider of mobility solutions, owning and operating the Enterprise Rent-A-Car, National Car Rental and Alamo Rent A Car brands through its integrated global network of independent regional subsidiaries. Enterprise Mobility and its affiliates offer extensive car rental, carsharing, truck rental, fleet management, retail car sales, as well as travel management and other transportation services, to make travel easier and more convenient for customers.   

Privately held by the Taylor family of St. Louis, Enterprise Mobility together with its affiliate Enterprise Fleet Management manages a diverse fleet of 2.4 million vehicles and accounted for nearly $39 billion in revenue through a network of more than 9,500 fully-staffed neighborhood and airport rental locations in more than 90 countries and territories. 

As we continue to build a team that drives us forward, we are excited to announce the opening for a Data Scientist.

ABOUT THE ROLE          

The Data Scientist is a key driver of innovation, transforming data into actionable insights that improve business processes. In this role, you’ll develop cutting-edge analytical products—creating algorithms for automation, building predictive models, designing experiments, and applying causal inference techniques to observational data. You’ll also harness mathematical optimization to identify the most profitable business strategies. Success in this position requires strong collaboration with both technical and non-technical teams to ensure the creation, delivery, and adoption of impactful analytical solutions

This position offers the opportunity to work fully remote within the United States (except for Alaska and/or Hawaii). Team members who choose virtual / remote work should have an adequate space to serve as their home office, and must be able to work a schedule within U.S. Central Standard Time core business hours. This position will require employees to come on site to one of our St. Louis campus locations a few times per year for meetings/events or as needed. #LI-REMOTE

We are committed to a fair and transparent hiring process. Candidates should expect identity verification, video interviews, technical validation of skills, and verification of employment, education, and work authorization. Falsification of information, proxy interviewing, or misrepresentation of experience or location will result in disqualification.


As a Data Scientist focused on revenue management, you will design and deploy advanced deep learning models to forecast demand. These models will enable branch-level decision-making, helping maximize revenue by leveraging historical trends and predictive analytics. In this role, you will collaborate closely with cross-functional teams to develop and implement analytical solutions that drive measurable business impact.

  • Collaborate with the team to design and deliver analytical solutions that drive business impact
  • Extract, clean, and manipulate structured and unstructured data from multiple sources
  • Perform exploratory data analysis to identify patterns, trends, and insights
  • Develop predictive models to support data-driven decision-making
  • Design and oversee experiments, ensuring accurate execution and interpretation of results
  • Apply causal inference techniques using observational data to uncover relationships
  • Prepare and deliver clear documentation of methodologies, findings, and recommendations
  • Create and present insightful reports and presentations for technical and non-technical audiences
  • Partner with cross-functional teams to implement and operationalize analytical solutions

Equal Opportunity Employer/Disability/Veterans


Required:

  • Must be presently authorized to work in the U.S. without a requirement for work authorization sponsorship by our company for this position now or in the future
  • Must reside in the United States (does not include Alaska or Hawaii)
  • Must be able to work a schedule within U.S. Central Standard Time core business hours.
  • Must have a Master’s Degree in a Statistical or Mathematical field (e.g. Engineering, Social Science, or Statistics)
  • Must have two (2+) years of experience with predictive models, statistical inference and deep learning
  • Must have experience using libraries like tensorflow or pytorch
  • Must have experience preparing and giving presentations to technical and non-technical audiences
  • Must have proficiency in R or Python
  • Must be committed to incorporating security into all decisions and daily job responsibilities

Preferred:

  • Doctorate Degree in a Statistical or Mathematical field (e.g. Engineering, Social Science, or Statistics)
  • Experience designing experiments
  • Experience exploring and visualizing data
  • Experience using Linux/Unix
  • Experience using SQL
  • Experience working with data (merging, recording, etc.) from a variety of sources/formats
  • Experience working with observational data to attempt causal inference (e.g. matching, weighting, etc.)

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