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About This Role
Clearance Level
None
Category
Data Science and Data Engineering
Location
Remote, Working from the USA
Key Skills For Success
Big Data
Data Analytics
Health Data
Structured Query Language (SQL) Development
##### REQ\#:RQ222488
##### Public Trust:None
##### Requisition Type:Regular
##### Your Impact
Own your opportunity to work alongside federal civilian agencies. Make an impact by providing services that help the government ensure the well being and support of U.S. citizens.
Job Description
-------------------
Our work depends on a Data Scientist Manager joining our team to support the Centers for Medicare \& Medicaid Services (CMS) anti‑fraud activities. As a Data Scientist Manager supporting the Healthcare Fraud Prevention Partnership (HFPP), you will lead a team of Data Scientists engaged in Fraud, Waste, and Abuse (FWA) analytics. You will collaborate with FWA Subject Matter Experts and Business Intelligence Developers to develop models and algorithms that detect and describe actionable FWA leads for the HFPP.
The Data Scientist Manager directs the Data Science team in using tools such as SAS and Python to analyze healthcare claims across Medicare, Medicaid, and private payers within a multi‑billion‑record database representing a large public‑private payer partnership.
HOW A DATA SCIENTIST MANAGER WILL MAKE AN IMPACT:
- Manage a team of Data Scientists working within a multidisciplinary organization of Data Scientists, Business Intelligence Analysts, and FWA Subject Matter Experts.
- Plan, direct, and oversee the development and execution of FWA models and algorithms in multi\-payer environments. This includes creating, reviewing, and maintaining statistical analysis plans; interpreting data; and generating reports to address research questions.
- Facilitate collaboration between the Data Science team, FWA SMEs, Business Intelligence Developers, and clients to develop meaningful analyses and provide predictive analytic support.
- Direct the design and maintenance of automated data pipelines, in partnership with data engineers and Business Intelligence Developers, feeding analytic outputs to the server environment for HFPP partner access.
- Deliver strong, compelling presentations to clients and stakeholders, effectively communicating insights and value propositions from TTP analytic outputs.
- Split responsibilities between management (40%) and hands‑on development/coding (60%).
- Oversee quality reviews of analytic outputs to ensure methodological integrity, adherence to protocol, and compliance with applicable access standards.
- Document processes, best practices, and strategies for current and future FWA models and algorithms, optimizing business efficiencies and process improvements.
- Manage a team of 5–8 Data Scientists.
WHAT YOU'LL NEED TO SUCCEED:
- Master’s degree in Statistics or a related field, or an equivalent combination of education and experience.
- 7\+ years of experience in statistical and computer science–related fields.
- Experience using SQL and Python for healthcare FWA data mining and statistical modeling.
- Experience managing client interactions, gathering and synthesizing requirements, and communicating complex analytic outcomes clearly.
- Proven experience presenting FWA analytic outcomes to clients, stakeholders, or conferences.
- Expertise in medical terminology and healthcare coding systems (ICD‑10, CPT, HCPCS, DRG, etc.).
- Experience conducting FWA data mining in large, multi\-payer databases spanning commercial insurance and public plans (Medicare and Medicaid).
- Experience researching, interpreting, and applying payer medical policies (LCDs, NCDs, private carrier policies) and industry claim edits (NCCI).
- Prior experience managing analytics teams, with strong supervisory and mentorship capabilities.
- Proficiency with the Microsoft Office Suite, including Word, Excel, and PowerPoint (portfolio samples or practicum assignments may be requested).
DESIRED QUALIFICATIONS:
- Experience with AWS and/or Snowflake environments.
- Ability to communicate technical outcomes in detailed, precise terms to technical audiences, while also explaining them in accessible ways for non‑technical audiences.
- Strong problem‑solving skills, accuracy, organizational skills, attention to detail, and the ability to multitask under deadlines.
- Excellent written and verbal communication skills, especially the ability to convey data clearly to clients.
- Ability to work in a fast‑paced, team‑oriented environment with minimal supervision.
SECURITY CLEARANCE REQUIREMENTS:
- Must be eligible to obtain a public trust clearance, requiring U.S. residency for 3 of the last 5 years.
WHAT GDIT CAN OFFER YOU:
The GDIT HFPP TTP is the only data warehouse of its kind, containing billions of healthcare claims from dozens of public and private payers. This environment exists exclusively to perform FWA analytics.
The TTP identifies overpayments, fraud leads, and insights into abusive billing behaviors through a cross‑payer lens. The HFPP is a public‑private partnership dedicated to combating fraud, waste, and abuse in healthcare.
We offer competitive pay and benefits, a flexible schedule, and a collaborative, interdisciplinary environment where innovation thrives.
\#GDITPublicHealthJobs
\#GDITJobs
### Work Requirements
Years of Experience
7 \+ years of related experience
- may vary based on technical training, certification(s), *or* degree
Certification
Travel Required
10\-25%
### Salary and Benefit Information
The likely salary range for this position is $129,813 \- $167,900\. 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.
### 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*
Salary Context
This $129K-$167K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At General Dynamics Information Technology, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($148K) sits 23% below the category median. Disclosed range: $129K to $167K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span Bethesda, MD, US, Springfield, VA, US, Fort Bragg, NC, US. Compensation range: $164K - $304K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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