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Healthcare Data Scientist (Medicare/Medicaid)

General Dynamics Information Technology•USA MD Home Office (MDHOME), MD
•$80,750 - $109,250•Remote

About The Position

GDIT is seeking a Healthcare Data Scientist to support efforts to detect and prevent fraud, waste, and abuse (FWA) in Medicare and Medicaid programs. The role offers the opportunity to develop analytical and AI skills while working with experienced data scientists and engineers on analytics and generative-AI initiatives that strengthen program integrity. The position supports data science, machine-learning, and generative-AI projects. Work includes exploratory data analysis, statistical analysis, foundational supervised and unsupervised learning, predictive modeling, anomaly detection, and helping develop components of generative-AI solutions. The role also involves preparing datasets, creating visualizations, documenting work, and communicating findings with colleagues and partners. This is a remote position with 40 scheduled hours per week and less than 10% travel required. The likely salary range is $80,750–$109,250, though actual compensation may vary based on experience, location, and contractual requirements. GDIT offers benefits including medical, dental, vision, and 401(k) plans, flexible work weeks where possible, paid leave and holidays, paid family leave, and disability and insurance offerings.

Requirements

  • Bachelor’s degree with an analytical or technical focus, such as Statistics, Computer Science, Engineering, or Applied Mathematics.
  • 1–3 years of experience in data science, analytics, or a related quantitative field.
  • Experience using Python or R for data analysis and machine-learning tasks.
  • Familiarity with data science libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, or Seaborn.
  • Exposure to generative AI or modern machine-learning frameworks, such as Hugging Face Transformers, LangChain, embeddings, or vector databases.
  • Familiarity with SQL and cloud data tools such as Databricks, Snowflake, or Spark.
  • Understanding of basic statistics and foundational machine-learning concepts, including feature engineering, train/test splits, and evaluation metrics.
  • Ability to create and interpret data visualizations.
  • Must be able to obtain NACI (T1) public trust clearance.

Nice To Haves

  • Experience working with healthcare data, particularly Medicare or Medicaid claims.
  • Knowledge of program integrity, healthcare analytics, or fraud, waste, and abuse detection concepts.
  • Prior exposure to anomaly detection, predictive modeling, or fraud-related analytics.

Responsibilities

  • Support analytical tasks across data science, machine-learning, and generative-AI projects.
  • Assist with exploratory data analysis and apply statistical methods and basic supervised and unsupervised learning techniques.
  • Help build generative-AI solution components, such as prompt pipelines, simple agents, embedding workflows, or retrieval-augmented systems.
  • Contribute to predictive modeling, anomaly detection, and other analytics used to identify potential fraud, waste, and abuse.
  • Build and maintain clean, well-structured datasets using Python and SQL-based tools.
  • Create visualizations to present trends, patterns, and model insights.
  • Document work and communicate findings with team members and partners.
  • Perform additional duties as assigned.

Benefits

  • Medical plan options, including options with Health Savings Accounts
  • Dental plan options
  • Vision plan
  • 401(k) plan with company match
  • Full-flex work weeks where possible
  • Paid time off, including vacation, sick and personal time
  • Paid holidays
  • Paid parental leave
  • Military leave
  • Bereavement leave
  • Jury duty leave
  • Paid Family Leave of up to 160 hours in a rolling 12-month period for eligible employees
  • Short- and long-term disability benefits
  • Life insurance
  • Accidental death and dismemberment insurance
  • Personal accident insurance
  • Critical illness insurance
  • Business travel and accident insurance

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