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About This Role
AI and ML Data ScientistThe Opportunity:
As an Agentic AI Engineer and Data Scientist for military intelligence, you’re excited by the opportunity to design, develop, and deploy advanced AI systems that help analysts transform complex, fragmented information into actionable intelligence. You understand the possibilities created by large language models (LLMs), machine learning (ML), natural language processing (NLP), autonomous workflows, and multi\-agent architectures, and you want to apply them to mission\-critical national security challenges.
In today’s contested and information\-rich operating environment, military intelligence teams must rapidly make sense of large volumes of structured and unstructured data from multiple sources, formats, and domains. As an AI professional at Booz Allen, you’ll help build intelligent systems that support intelligence discovery, analysis, prioritization, reporting, and decision advantage for defense and national security clients.
On our team, you’ll use your AI, data science, and software development skills to create real\-world mission impact. You’ll work closely with clients, analysts, engineers, and mission stakeholders to understand operational needs, identify high\-value use cases, and develop agentic AI capabilities that can reason over data, orchestrate workflows, retrieve relevant information, generate analytic products, and support human\-in\-the\-loop decision\-making. You’ll design and implement AI agents, retrieval\-augmented generation pipelines, evaluation frameworks, prompt strategies, data processing workflows, and mission\-focused prototypes. You’ll help ensure these systems are reliable, explainable, secure, testable, and aligned to operational requirements. Ultimately, you’ll help military intelligence organizations use AI responsibly and effectively to accelerate insight, improve analytic tradecraft, and support informed decisions.
Work with us as we develop advanced AI capabilities for the mission.
Join us. The world can’t wait.
You Have:
- Experience developing, integrating, or evaluating AI, ML, NLP, LLMs, or agentic AI capabilities for mission, operational, or enterprise use cases, and building AI\-enabled applications using Python and modern AI/ML libraries or frameworks, including PyTorch, Scikit\-Learn, LangChain, LlamaIndex, Hugging Face, or CUDA
- Experience developing agentic AI systems, including autonomous or semi\-autonomous agents, tool\-using agents, multi\-step reasoning workflows, task orchestration, retrieval\-augmented generation, or human\-in\-the\-loop AI capabilities
- Experience analyzing, processing, and integrating structured and unstructured data sources, including intelligence data such as text, reports, messages, metadata, documents, or geospatial data
- Experience designing, testing, validating, or evaluating AI/ML systems, including model performance, accuracy, relevance, robustness, hallucination mitigation, or mission\-aligned evaluation criteria
- Experience supporting military intelligence, defense intelligence, all\-source analysis, targeting, indications and warning, collection management, operations intelligence, or national security missions, and developing AI systems for classified, air\-gapped, secure, or operationally constrained environments
- Experience with prompt engineering, prompt evaluation, model benchmarking, fine\-tuning, synthetic data generation, or LLM application development
- Knowledge of information retrieval, embeddings, vector databases, semantic search, data labeling, classification models, model evaluation, and data quality assessment
- Ability to translate military intelligence mission requirements into technical AI solutions, prototypes, and production\-ready capabilities
- TS/SCI clearance
- Bachelor’s degree in Data Science, CS, AI, ML, Engineering, Operations Research, Applied Mathematics, or Statistics
Nice If You Have:
- Experience with distributed data, cloud, or high\-performance computing tools, including Spark, Kafka, Hadoop, Hive, EMR, Databricks, Kubernetes, Docker, OpenSearch, Elasticsearch, or similar technologies
- Experience developing APIs, microservices, analytic interfaces, dashboards, or production software using FastAPI, Flask, REST services, Git, CI/CD, DevSecOps, Plotly, Dash, Streamlit, Tableau, or Power BI
- Knowledge of intelligence tradecraft, analytic standards, military operations, Joint or Combatant Command environments, the intelligence cycle, AI safety, model governance, responsible AI, explainability, auditability, access control, or data security
- Ability to collaborate with analysts, engineers, data owners, security teams, and mission stakeholders to deploy AI\-enabled capabilities in operational environments
- TS/SCI clearance with a polygraph
- Master's degree in Data Science, CS, AI, ML, Engineering, Operations Research, Applied Mathematics, Statistics, or a related field
- AI, ML, Cloud Computing, Data Engineering, Cybersecurity, or DevSecOps Certification such as AWS, Azure, Google Cloud, Databricks, Kubernetes, Security\+, or similar Certification
Clearance:
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance is required.
Compensation
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well\-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work\-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full\-time and part\-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract\-specific affordability and organizational requirements. The projected compensation range for this position is $77,600\.00 to $176,000\.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the Posting Date.Identity Statement
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 interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Candidate AI Usage Policy
AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in\-person or virtual) is prohibited unless permission is explicitly provided.
Work Model
Our people\-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.
- Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.
- Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.
- Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.
Commitment to Non\-Discrimination
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.
Salary Context
This $77K-$176K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 245 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 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Booz Allen Hamilton, 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 868 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($126K) sits 36% below the category median. Disclosed range: $77K to $176K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Booz Allen Hamilton AI Hiring
Booz Allen Hamilton has 40 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI Software Engineer, AI/ML Engineer. Positions span McLean, VA, US, Honolulu, HI, US, El Segundo, CA, US. Compensation range: $126K - $303K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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