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About This Role
Overview:
FTI Defense is seeking transitioning Navy service members who want to continue supporting the Navy mission while building the next chapter of their careers in data science, predictive analytics, and AI centric solutions. This opportunity is designed for Sailors who have been performing data analytics, operational analysis, modeling, programming, intelligence analysis, operations research, safety analysis, or related technical work during their military service. We recognize that the experience gained while serving may not look like a traditional commercial Data Scientist resume. What matters is the work you have performed, the technical skills you have developed, and your understanding of the Navy mission and operational environment. As a member of FTI’s data science team supporting the Naval Safety Command in Norfolk, VA, you will work alongside Data Scientists and Software Engineers to develop, test, and deploy advanced predictive analytics models. These models use Navy data to help identify and predict precursors to mishaps and safety hazards.
This is a hybrid position with onsite work at the Naval Safety Command Center in Norfolk, VA. An active DoD Secret Clearance is required.
Responsibilities: What You Will Work On* Support the design, calibration, and testing of predictive risk models used to evaluate mishap risk across Navy communities.
- Analyze operational data to identify relationships, trends, unusual patterns, and potential indicators of risk.
- Apply techniques such as statistical modeling, forecasting, time series analysis, anomaly detection, and predictive analytics.
- Support feature engineering, model testing, cross validation, and performance evaluation.
- Use Python, R, or similar programming languages and analytical tools to solve real world Navy challenges.
- Partner with Data Scientists and Software Engineers to transition analytical models into operational environments.
- Participate in technical exchanges with Navy customers and help translate operational requirements and mission needs into analytical solutions.
- Apply your existing understanding of Navy operations, terminology, processes, and mission requirements while expanding your technical experience in AI and data science.
Education/Qualifications: What We Are Looking For* Active Department of Defense Secret Clearance.
- Current or transitioning Navy service member with experience performing data analytics, operational analysis, programming, modeling, statistics, operations research, intelligence analysis, safety analysis, or other related technical work.
- Hands on experience working with data to identify trends, solve problems, support decisions, or improve operational outcomes.
- Experience with Python, R, or another relevant programming or analytical language.
- Exposure to analytical or machine learning tools such as Pandas, NumPy, Scikit learn, SciPy, or comparable technologies.
- Understanding of statistical analysis, predictive modeling, probability, forecasting, simulation, machine learning, or related analytical methods gained through military training and experience.
Additional Experience That Would Be Valuable
Experience with Navy data environments, operational or safety data, data visualization, dashboards, Qlik, Tableau, Databricks, Apache Spark, or Python based visualization tools is beneficial but not required. Continue the Mission in a New Role
Your transition from military service does not have to mean leaving the Navy mission behind. FTI offers an opportunity to take the operational knowledge, technical experience, and mission understanding you developed while serving and apply it to the next generation of AI, data science, and predictive analytics solutions supporting the Navy. You already understand the mission. We can help you continue building the technical career around it.
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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At FTI Defense, 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 1,503 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
FTI Defense AI Hiring
FTI Defense has 2 open AI roles right now. They're hiring across Data Scientist. Based in Norfolk, VA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.
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