Hiring.Camp

Healthcare Data Scientist (Medicare/Medicaid)

Gdit

·

Yesterday

Salary
$85k – $115k
Location
USA MD Home Office (MDHOME), United States of America
Workplace
Remote
Type
Full-time
Department
Healthcare
Experience
2+ years
Source
Workday

Description

Type of Requisition:

Regular

Clearance Level Must Currently Possess:

None

Clearance Level Must Be Able to Obtain:

None

Public Trust/Other Required:

NACI (T1)

Job Family:

Data Science and Data Engineering

Job Qualifications:

Skills:

Python (Programming Language), R Programming, Science

Certifications:

None

Experience:

2 + years of related experience

US Citizenship Required:

No

Job Description:

We are GDIT. We take pride in providing our clients with the data they need to make important decisions that impact the world around us.

You make GDIT your place by delivering insights to help our clients make impactful changes real. By owning your opportunity at GDIT, you’ll become a critical part in how we successfully solve our clients’ biggest challenges.

Our work is looking for a Data Scientist eager to grow their analytical and AI skillset while supporting vital efforts to detect and prevent fraud, waste, and abuse (FWA) in the Medicare and Medicaid programs. In this role, you will work alongside experienced data scientists and engineers, contributing to advanced analytics and generative‑AI initiatives.

Responsibilities:

  • Supporting analytical tasks across data science, machine learning, and generative‑AI projects.
  • Assisting with the analysis of Medicare and Medicaid claims data using statistical methods and supervised/unsupervised learning techniques.
  • Helping develop components of generative‑AI solutions, including prompt pipelines, simple agents, embedding workflows, or retrieval‑augmented systems.
  • Contributing to predictive modeling, anomaly detection, and other program‑integrity analytics.
  • Building and maintaining clean datasets using Python and SQL-based tools.
  • Creating clear visualizations to present trends, patterns, and model insights.
  • Documenting work and communicating findings with team members and partners.
  • Performing additional duties as assigned.

Required Skills:

  • Bachelor’s degree with an analytical or technical focus (Statistics, Computer Science, Engineering, Applied Mathematics, etc.)
  • At least 2 years of experience in Data Science
  • Experience analyzing data, preferably healthcare or claims data
  • Familiarity with Python or R for data analysis and machine learning
  • Experience with common data science libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, or Seaborn.
  • Exposure to generative AI or modern machine learning frameworks (e.g., Hugging Face Transformers, LangChain, embeddings, vector databases)
  • Familiarity with SQL and cloud data tools such as Databricks, Snowflake, or Spark is a plus.
  • Understanding of basic statistics and foundational ML concepts such as feature engineering, train/test splits, and evaluation metrics.
  • Ability to create and interpret data visualizations.

The likely salary range for this position is $85,000 - $115,000. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.

Scheduled Weekly Hours:

40

Travel Required:

Less than 10%

Telecommuting Options:

Remote

Work Location:

Any Location / Remote

Additional Work Locations:

Total Rewards at GDIT:

Our benefits package for all US-based employees includes a variety of medical plan options, some with Health Savings Accounts, dental plan options, a vision plan, and a 401(k) plan offering the ability to contribute both pre and post-tax dollars up to the IRS annual limits and receive a company match. To encourage work/life balance, GDIT offers employees full flex work weeks where possible and a variety of paid time off plans, including vacation, sick and personal time, holidays, paid parental, military, bereavement and jury duty leave. GDIT typically provides new employees with 15 days of paid leave per calendar year to be used for vacations, personal business, and illness and an additional 10 paid holidays per year. Paid leave and paid holidays are prorated based on the employee’s date of hire. The GDIT Paid Family Leave program provides a total of up to 160 hours of paid leave in a rolling 12 month period for eligible employees. To ensure our employees are able to protect their income, other offerings such as short and long-term disability benefits, life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance are provided or available. We regularly review our Total Rewards package to ensure our offerings are competitive and reflect what our employees have told us they value most.

 

 


Our Identity Verification Process:

As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.

 

 

About Our Work:

We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50+ countries worldwide, offering leading mission-ready capabilities in AI, cloud, cyber and software development.

Join our Talent Community to stay up to date on our career opportunities and events at

gdit.com/tc.

Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans

Skills

PythonSQLMachine LearningPandasNumPyScikit-learnSparkSnowflakeDatabricksData ScienceData Engineering

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