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Data Scientist

Codoxo

Remote
  • Job Type: Full-Time
  • Function: Data Science
  • Industry: Insurance
  • Post Date: 02/05/2024
  • Website: www.codoxo.com
  • Company Address: 3190 Northeast Expressway NE, Suite 120, Atlanta, GA, 30341
  • Salary Range: $50,000 - $150,000

About Codoxo

At Codoxo, formerly Fraudscope, we’re on a mission to make our healthcare system more affordable and effective. Our Forensic AI Platform uses a patented algorithm to identify problems and suspicious behavior earlier than traditional techniques which helps ensure our scarce healthcare dollars go to real patient care.

Job Description

CODOXO IS NOT ABLE TO OFFER SPONSORSHIP OR ACCOMMODATE ANY CANDIDATES THAT ARE CURRENTLY BEING SPONSORED NOW OR IN THE FUTURE

 

Of the $3.8T we spend on healthcare in the United States annually, about a third of it is estimated to be lost due to waste, fraud and abuse.  Codoxo is the premier provider of artificial intelligence-driven solutions and services that help healthcare companies and agencies proactively detect and reduce risks from fraud, waste, and abuse and ensure payment integrity. Codoxo helps clients manage costs across network management, clinical care, provider coding and billing, payment integrity, and special investigation units. Our software-as-a service applications are built on our proven Forensic AI Engine, which uses patented AI-based technology to identify problems and suspicious behavior far faster and earlier than traditional techniques.

 

We are venture backed by some of the top investors in the country, with strong financials, and remain one of the fastest growing healthcare AI companies in the industry. 


Position Summary: 

 

We are looking for a highly motivated Healthcare Data Scientist to join our team. The ideal candidate must have a strong understanding of healthcare data and Payment Integrity concepts. Experience with Python, Pandas, and AWS is strongly preferred. The Data Scientist will be responsible for developing and implementing data-driven solutions to uncover valuable insights and improve healthcare payment integrity. This role will be core to furthering our Models as a Service Payment Integrity engine.

 

The ideal candidate will be able to utilize advanced statistical and machine learning techniques to analyze large-scale healthcare data sets, including medical claims, reimbursement data, and unstructured datasets. Develop predictive models to identify patterns, detect anomalies, and advance the detection and decisioning of payment integrity errors, waste, abuse, and other areas of Payment Integrity.

 

Responsibilities:

 

Apply your knowledge in developing and evolving Payment Integrity concepts and methodologies to enhance payment accuracy, minimize overpayments, and identify potentially fraudulent activities. Collaborate with cross-functional teams to develop innovative solutions for improving payment integrity processes.

 

  • Analyze large datasets of healthcare data to identify trends and patterns.
  • Develop new Payment Integrity detection algorithms that use machine learning to identify patterns in healthcare data that could indicate payment inaccuracies, overpayments, waste, abuse, and error.
  • Develop and deploy machine learning models to predict outcomes and improve decision-making.
  • Innovate and maintain detection solutions.
  • Communicate detection findings to stakeholders.
  • Providing training and support to other healthcare professionals on fraud detection.
  • Staying up-to-date on the latest detection techniques and technologies.

 

Core Qualifications:

 

  • Expertise in data mining and machine learning techniques to identify patterns and anomalies.
  • 2 to 3 years developing and implementing healthcare Payment Integrity solutions.
  • Proficient in ideating.
  • 1 to 2 years ideating new Payment Integrity models and rules.
  • Programming skills in Python, Pandas, and AWS tools. 
  • Ability to grasp new concepts quickly.
  • Creative problem-solving skills with a passion for innovation.
  • Strong communication and presentation skills. 
  • Ability to work independently and as part of a team. 

 

Associate Requirements:

 

  • Bachelor's or Master's degree in data science, statistics, a related field, or currently enrolled in a related program.
  • 1-2 years of experience in data analysis.
  • Core understanding of Python, Pandas, and AWS.
  • Experience with machine learning and predictive modeling.
  • Strong problem-solving and analytical skills.
  • Comfortable with communicating results in a manner that can be understood by all levels of the organization. 

 

Mid-level Requirements: 

 

  • Bachelor's or Master's degree in data science, statistics, or a related field.
  • 2-3+ years of experience in data analysis.
  • Strong understanding of Python, Pandas, and AWS.
  • Demonstrated experience with more advanced machine learning and predictive modeling.
  • Ability to work with stakeholders to understand and collaborate on concepts.
  • Excellent problem-solving and analytical skills.
  • Strong communication and presentation skills, able to effectively communicate with technical and non-technical audiences.
  • Prior experience in healthcare, Payment Integrity, and/or fraud, waste, and abuse.

 

Senior Requirements:

 

  • Master's degree in data science, statistics, or a related field preferred.
  • 4-6+ years of experience in data analysis.
  • Strong understanding of and ability to mentor others on Python, Pandas, and AWS.
  • Creative ability to innovate on more advanced machine learning and predictive modeling solutions.
  • Exceptional problem-solving and advanced analytical skills.
  • Strong communication and presentation skills, able to effectively communicate with technical and non-technical audiences and customers.
  • Ability to mentor, support, and lead the development efforts of cross-organizational projects and/or other team members.
  • Strong understanding of healthcare payment systems and processes. This includes knowledge of how healthcare claims are processed, how payments are made, and the different types of payment integrity concepts, fraud, waste, and abuse that can occur.

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