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About This Role
Clearance Level Top Secret Category Data Science and Data Engineering Locations Fort Bragg, North Carolina
Jacksonville, North Carolina
Joint Base Lewis-McChord, Washington
Fort Campbell, Kentucky (Onsite Workplace)
Key Skills For Success Data Analysis
Data Analytics
Data Science
Data Visualization
Statistics
REQ#:RQ229928
Public Trust:None
Requisition Type:Regular
Your Impact
Own your opportunity to serve as a critical component of our nation’s safety and security. Make an impact by using your expertise to protect our country from threats.
Job Description
We are GDIT. The people supporting and securing some of the most complex government, defense, and intelligence projects across the country. We ensure today is safe and tomorrow is smarter. Our work has meaning and impact on the world around us, but also on us, and that’s important.
GDIT is your place. You make it your own by embracing autonomy, seizing opportunity, and being rusted to deliver your best every day.
We think. We act. We deliver. There is no challenge we can’t turn into opportunity. And our work depends on a Data Scientist joining our team to support customer activities.
The Data Scientist applies statistical analysis, data mining, and machine learning to human performance, behavioral health, and readiness data to produce actionable insights, forecasts, and decision-support products for the POTFF enterprise. The Data Scientist frames analytical questions with Government stakeholders, prepares and explores data, selects and applies appropriate methods, validates results, and communicates findings to technical and non-technical audiences through reports, briefings, and visual analytics built on the Government-furnished technology stack.
HOW DATA SCIENTIST WILL MAKE AN IMPACT:
- Query, clean, transform, and prepare data from the Government-designated Human Performance System of Record (e.g., HPDMS) and other Government-approved sources for analysis, using the curated datasets maintained by the Data Engineer where available.
- Develop, validate, and document predictive and prescriptive models, forecasting methods, clustering, and other advanced statistical techniques to generate actionable forecasts regarding operator readiness, injury risk, program efficacy, and performance optimization, as directed by the Government.
- Support text-heavy use cases using Natural Language Processing (NLP) techniques (e.g., cleaning, entity extraction, classification) to analyze unstructured clinical or survey data and apply Large Language Model (LLM) and agent-style workflows only within Government-approved tools and datasets.
- Develop, deploy, and maintain visual analytics, dashboards, and executive reports using Government-furnished tools (e.g., Power BI, including semantic modeling and DAX), and integrate data products into low-code operational workflows (e.g., Microsoft Power Platform) where directed.
- Support Government-designed analyses and experiments that measure the validity, efficacy, and Return on Investment (ROI) of POTFF interventions, including computation of program-efficacy metrics as directed by the Government.
- Automate recurring analyses and the generation of required monthly and quarterly reports, reducing manual effort and improving accuracy.
- Document data sources, transformations, assumptions, methods, and validation procedures so that analyses are reproducible and can be transferred to other personnel.
- Communicate complex concepts, methods, and results clearly to non-technical command leadership and to clinical and performance staff, in writing and in briefings.
- Collaborate with embedded clinical and performance staff to identify opportunities for capturing data and improving the quality of input into Government systems.
- Maintain rigorous data quality, security, and privacy controls, including DoW Impact Level 5 (IL5), HIPAA, and Privacy Act protocols, in all analytic work.
WHAT YOU’LL NEED TO SUCCEED:
EDUCATION: Bachelor’s degree in a quantitative discipline, including but not limited to statistics, biostatistics, mathematics, applied mathematics, data science, operations research, computer science, engineering, physics, economics or econometrics, epidemiology, geospatial science or geoinformatics, or psychology or another behavioral science with a quantitative-methods concentration, with documented coursework or professional application in statistics, programming, and quantitative analysis.
- Minimum of one (1) year of experience applying data science or statistical methods to real-world problems. A Bachelor’s degree program that included documented internship, cooperative-education, capstone, thesis, or project work applying data science methods to real-world data satisfies this requirement.
- Demonstrated proficiency in Python or R for data analysis, statistical modeling, and machine learning, and in SQL for querying relational data, demonstrated through professional work experience or degree-program projects.
- Working knowledge of applied statistics and of supervised and unsupervised machine-learning methods.
- Ability to prepare and clean data for analysis, including familiarity with data engineering concepts (ETL/ELT, data modeling basics, reproducible workflows, and version control with Git).
- Experience producing visual analytics and reports for decision-makers (Power BI preferred).
- Ability to communicate clearly with stakeholders and translate mission needs into analytical questions and outputs.
PREFERRED QUALIFICATIONS:
- Experience with Databricks and Apache Spark (PySpark) or a comparable distributed processing system.
- Familiarity with NLP and LLM use cases, including text preparation, evaluation datasets, and governance considerations.
- Familiarity with the Microsoft Power Platform (Power Apps and Power Automate) and with Palantir Foundry.
- Experience with web application development technologies (e.g., HTML, CSS, JavaScript, React, Django, or ASP.NET Core) or with deploying analytics into applications. Software engineering or web-development experience is not required.
- Experience with human performance, sports science, health, clinical, or biomedical data.
LOCATION: Various CONUS SITES
CLEARANCE: Secret or Top-Secret Clearance, or the ability to obtain clearance.
This is a contingent posting, expected to start in 2027.
GDIT IS YOUR PLACE:
- 401K with company match
- Comprehensive health and wellness packages
- Internal mobility team dedicated to helping you own your career
- Professional growth opportunities including paid education and certifications
- Cutting-edge technology you can learn from
- Rest and recharge with paid vacation and holidays
#MilitaryHealthGDITJobs
Work Requirements
Years of Experience 0 + years of related experience
- may vary based on technical training, certification(s), or degree
Certification Travel Required Less than 10%
Citizenship U.S. Citizenship Required
Salary and Benefit Information
The likely salary range for this position is $72,877 - $98,599. 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 $72K-$98K range is in the lower quartile for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($85K) sits 55% below the category median. Disclosed range: $72K to $98K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span Tampa, FL, US, Joint Base Lewis McChord, WA, US, Hurlburt Field, FL. Compensation range: $98K - $253K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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