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About This Role
Your Impact
Own your opportunity to support our nation's defense. Make an impact by connecting and securing critical operations across the globe, keeping our country safe and secure.
Job Description
We are seeking a Senior Data Scientist to design, train, evaluate, and deliver machine learning models that solve operational problems across USCENTCOM’s Data Office initiatives. This is a hands\-on ML practitioner role—not a platform or infrastructure position. The Senior Data Scientist will work within an established on\-premises Data Analytical Environment (DAE) built on a Data Lakehouse architecture with H100 GPU infrastructure, applying their expertise in statistical modeling, deep learning, and applied ML to turn enterprise data into actionable intelligence. The ideal candidate brings deep experience in model development across multiple problem domains—forecasting, NLP, anomaly detection, and classification—and can independently lead the ML practice for the team.
WHAT YOU WILL BE DOING:
Model Development \& Training
- Design, train, and validate supervised, unsupervised, and deep learning models using open\-source libraries (PyTorch, TensorFlow, Scikit\-learn, XGBoost, LightGBM) to support forecasting, classification, anomaly detection, and NLP use cases
- Conduct rigorous experiment design: feature engineering, hyperparameter tuning, cross\-validation, and evaluation using appropriate metrics (precision/recall/F1, RMSE, AUC\-ROC) to ensure production\-quality model performance
- Fine\-tune and adapt open\-source LLMs (LLMA, Mistral, and similar) for domain\-specific tasks including document summarization, entity extraction, and question\-answering over classified and unclassified networks
- Develop and maintain RAG pipelines: chunking strategies, embedding model selection, retrieval evaluation, and prompt engineering to deliver high\-quality LLM\-augmented analytics
Applied Problem\-Solving
- Translate mission requirements into ML solutions: work directly with analysts, operators, and leadership to scope problems, define success criteria, and deliver models that produce actionable operational insights
- Build models across multiple domains including predictive analytics (logistics, readiness), NLP/text analytics (reports, intelligence documents), anomaly detection (cybersecurity, network, behavioral), and computer vision where applicable
- Design lightweight, optimized models for edge and disconnected environments when required, supporting model optimization and conversion (ONNX, TensorRT, OpenVINO) for tactical deployment
MLOps \& Lifecycle (Collaborative)
- Version, track, and reproduce experiments using MLflow, DVC, and Git; maintain clear documentation of model lineage, training data, and performance baselines
- Package trained models for deployment in containerized environments (Docker, Kubernetes) in coordination with the platform engineering team. Ownership of deployment infrastructure is flexible and project\-dependent
- Integrate models into existing CI/CD pipelines, analytics platforms, and decision support tools in collaboration with the DevSecOps and data engineering teams
Data Security \& Compliance
- Ensure all model development adheres to DoD security, encryption, and data handling standards, including tagging, metadata management, and retention policies
- Operate within classified environments (SIPR/NIPR), following cybersecurity and data stewardship protocols across air\-gapped and hybrid infrastructure
WHAT YOU WILL NEED:
Education \& Experience
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, Data Science, or related quantitative field
- 8\+ years of hands\-on AI/ML model development experience with a strong record of delivering production models, not just prototypes
- Compliant with DoD Directive 8140 (i.e., CompTIA Security \+ CE cert)
- Active Secret clearance is required. Must be TS/SCI eligible
- Must be able to work on site at MacDill AFB. Not a remote role.
Technical Skills
- Strong Python proficiency and deep experience with open\-source ML frameworks (PyTorch, TensorFlow, Scikit\-learn, XGBoost, LightGBM, Hugging Face Transformers)
- Demonstrated ability to train, fine\-tune, and evaluate models end\-to\-end—from raw data through feature engineering, model selection, training, validation, and production handoff
- Experience with LLM fine\-tuning techniques (LoRA, QLoRA, PEFT) and RAG architecture design (vector databases, embedding strategies, retrieval evaluation)
- Working knowledge of MLOps toolchains (MLflow, DVC, Weights \& Biases) and version control (Git).
- Familiarity with containerized deployment (Docker, Kubernetes) in air\-gapped or on\-premise environments
- Experience working with large\-scale data systems and medallion/lakehouse architectures
DESIRED QUALIFICATIONS
- Experience with model optimization and conversion (ONNX, TensorRT, OpenVINO) for edge or tactical deployment
- Knowledge of NLP techniques applied to defense or intelligence domains (entity extraction, document classification, summarization of operational reports)
- Familiarity with distributed data frameworks (Apache Spark, Dask)
- Experience with edge AI hardware (NVIDIA Jetson, Coral TPU)
WHAT GDIT CAN OFFER :
At GDIT, the mission is our purpose, and our people are at the center of everything we do.
- Growth: AI\-powered career tool that identifies career steps and learning opportunities
- Support: An internal mobility team focused on helping you achieve your career goals
- Rewards: Comprehensive benefits and wellness packages, 401K with company match, competitive pay and paid time off
- Community: Award\-winning culture of innovation and a military\-friendly workplace
\#ARMA
\#GDITPRIORITY
\#CENTCOM/CITS
Work Requirements
Years of Experience
8 \+ years of related experience* may vary based on technical training, certification(s), *or* degree
Certification
Certified Data Scientist (Open CDS) \| The Open Group \- The Open Group
Microsoft Certified: Azure Data Scientist Associate (DP\-100\) \| Microsoft \- Microsoft
CompTIA Security\+ CE \| CompTIA \- CompTIA
Certified Entry Level Python Programmer (PCEP) \| Python Institute (PI) \- Python Institute (PI)
Travel Required
Less than 10%
Citizenship
U.S. Citizenship Required
Salary and Benefit Information
The likely salary range for this position is $153,000 \- $207,000\. 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.
View information about benefits and our total rewards program.
About Our Work
We are GDIT. A global technology and professional services company that delivers technology 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 over 50 countries worldwide, offering leading capabilities in digital modernization, AI/ML, cloud, cyber and application development. Together with our customers, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology.
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 $153K-$207K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 9% below the category median. Disclosed range: $153K to $207K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span IN, US, Fort Bragg, NC, US, Tampa, FL, US. Compensation range: $103K - $207K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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