Interested in this Data Scientist role at General Dynamics Information Technology?
Apply Now →Skills & Technologies
About This Role
Clearance Level Secret Category IT Infrastructure and Operations Location Tampa, Florida (Onsite Workplace)
Key Skills For Success Artificial Intelligence (AI)
Development Design
Machine Learning (ML)
Modeling
REQ#:RQ229587
Public Trust:None
Requisition Type:Regular
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
Own your opportunity to turn data into measurable outcomes for our customers’ most complex challenges. As a Data Scientist Journeyman at GDIT, you’ll power innovation to drive mission impact and grow your expertise to power your career forward.
MEANINGFUL WORK AND PERSONAL IMPACT
As a Data Scientist Journeyman, the work you’ll do at GDIT will be impactful to the mission of USCENTCOM’s mission to become a data-centric organization. You will play a crucial role in supporting analytics, automation, and intelligent system development within a secure, on-premise and hybrid environments.
- Use of open-source tools and platform for developing and deploying AI/ML models at scale.
- Deliver operational insights, automate mission workflows, and apply machine intelligence to high-priority defense challenges.
HOW A DATA SCIENTIST WILL MAKE AN IMPACT
Roles and Responsibilities:
- Develop and Train AI/ML Models + Create supervised, unsupervised, and deep learning models using open-source libraries (e.g., PyTorch, TensorFlow, Scikit-learn) to support forecasting, classification, anomaly detection, and autonomous system behavior.
- Deploy and Operationalize Models + Package and deliver models via containerized technologies (e.g., Docker, Podman, Kubernetes) for execution in on-premise and edge environments (e.g., Jetson, rugged laptops).
- Design Edge-Compatible ML Solutions + Build lightweight, optimized models for low-power or disconnected environments; support fielded capabilities requiring minimal compute and latency.
- Lead MLOps Implementation + Use tools such as MLflow, DVC, Kubeflow, and Git for model versioning, reproducibility, and secure lifecycle management.
- Collaborate with Multi-Disciplinary Teams + Partner with data scientists, DevSecOps teams, engineers, and analysts to align model development with operational requirements, and support AI integration into mission workflows.
- Enable Real-Time Sensor Integration + Develop models that consume real-time data from local sensors, devices, or systems (e.g., video, radar, comms), delivering actionable results with minimal delay.
- Ensure Data Security and Compliance + Engineer AI pipelines that adhere to DoD security, encryption, and data handling standards, including tagging, metadata management, and retention policies.
WHAT YOU’LL NEED TO SUCCEED
Bring your expertise and drive for innovation to GDIT. The Data Scientist Journeyman must have:
- Education: Bachelor Degree in Computer Science, Machine Learning, Data Science, Applied Mathematics, or related technical field.
- Certification: Compliant with DoD Directive 8140
- Experience: 3+ years of related experience
- Required skills: + Strong experience with Python, open-source ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM), and scientific computing frameworks. + Expertise in Data Management best practices, design and engineering for large-scale data systems + Ability to translate end-user’s high-level requirements into detailed analytics to be processed in SIEM, AI/ML custom and cloud-managed solutions. + Demonstrated ability to deploy models in air-gapped or on-prem environments, using Docker/Kubernetes/Podman. + Hands-on knowledge of MLOps toolchains (MLflow, DVC, Kubeflow), version control (Git), and secure DevSecOps principles. + Experience with model optimization and conversion (e.g., ONNX, TensorRT, OpenVINO) for tactical and edge deployment. + Proven ability to work in classified or secure DoD environments, following cybersecurity and data stewardship protocols. + Experience integrating models into CI/CD pipelines, analytics platforms, or decision support tools.
- Desired Skills: + Experience with edge AI hardware (NVIDIA Jetson, Coral TPU, rugged ARM/x86 systems). + Knowledge of NLP, computer vision, or reinforcement learning techniques applied to defense. + Familiarity with Apache Spark, Hadoop, and distributed data frameworks. + Background in real-time messaging protocols (e.g., MQTT, ZeroMQ, ROS) for edge-to-core communication. + Familiarity with USCENTCOM operational data systems, networks, and mission requirements. + Understanding of DoD AI/ML strategies, ethical AI standards, and responsible deployment principles.
- Security clearance level: Secret with ability to obtain Top Secret
- US citizenship required
GDIT IS YOUR PLACE
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
Work Requirements
Years of Experience 3 + years of related experience
- may vary based on technical training, certification(s), or degree
Certification CompTIA Security+ CE | CompTIA - CompTIA
Travel Required Less than 10%
Citizenship U.S. Citizenship Required
Salary and Benefit Information
The likely salary range for this position is $97,750 - $132,250. 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 $97K-$132K 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 ($115K) sits 39% below the category median. Disclosed range: $97K to $132K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.