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About This Role
About Peraton
Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world’s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees solve the most daunting challenges that our customers face. Visit peraton.com to learn how we’re keeping people around the world safe and secure.
About The Role
Position Overview
We are seeking an experienced Data Scientist / Subject Matter Expert (SME) to support a classified government contract. The successful candidate will provide technical expertise in analyzing and interpreting complex data to inform enterprise data management decisions, identify opportunities for the application of Data Science, and develop innovative solutions that address customer mission requirements.
The Data Scientist will support multiple customer organizations and Chief Artificial Intelligence Officer (CAIO) initiatives by identifying and evaluating opportunities to apply advanced analytics, artificial intelligence, and machine learning to customer data assets. The position will help bridge the gap between mission requirements, data assets, and emerging AI/ML capabilities.
The role requires extensive collaboration with government stakeholders, technical teams, external customers, and IC partners. The Data Scientist will contribute to the development and implementation of technical solutions, support integration of AI capabilities with existing customer data environments, and help establish repeatable approaches for leveraging Data Science across the enterprise.
Key Responsibilities
- Serve as a Data Science SME providing technical leadership and expertise across customer organizations and mission areas.
- Analyze, interpret, and derive insights from complex, heterogeneous, and large-scale datasets to support mission and enterprise decision-making.
- Identify opportunities to apply Data Science, advanced analytics, artificial intelligence, and machine learning to customer mission challenges.
- Assess customer data assets to determine their suitability for advanced analytics and AI/ML applications.
- Support CAIO initiatives by identifying, evaluating, prioritizing, and implementing Data Science and AI use cases.
- Translate mission and business requirements into Data Science problems, analytical approaches, technical requirements, and implementation strategies.
- Develop statistical models, machine learning models, algorithms, and analytical methodologies to address complex customer requirements.
- Design and develop prototypes and proof-of-concept solutions to demonstrate the potential value of Data Science and AI capabilities.
- Support the transition of successful prototypes and analytical capabilities into operational or production environments.
- Integrate AI and Data Science solutions with existing customer data assets, platforms, pipelines, and analytic environments.
- Collaborate with data engineers, data architects, software developers, AI/ML engineers, and other technical personnel to develop end-to-end solutions.
- Provide technical support to other customer organizations seeking to identify and implement Data Science opportunities.
- Coordinate with external customers, mission partners, IC organizations, and other stakeholders to define requirements and develop interoperable technical solutions.
- Participate in technical working groups, design sessions, and collaborative forums focused on Data Science, AI, and enterprise data capabilities.
- Evaluate emerging Data Science, AI/ML, and analytical technologies and recommend capabilities that can improve customer mission effectiveness.
- Assess data quality, availability, structure, provenance, and other characteristics that may affect analytical and AI solution development.
- Identify data management challenges and recommend improvements required to enable effective Data Science and AI applications.
- Develop reusable analytical methodologies, models, frameworks, and technical approaches that can be applied across multiple customer organizations.
- Support development of standards, best practices, and repeatable processes for Data Science experimentation, evaluation, implementation, and operationalization.
- Develop analytical documentation, technical specifications, model documentation, methodologies, and implementation plans.
- Validate analytical models and results and communicate assumptions, limitations, risks, and findings to technical and non-technical stakeholders.
- Brief government leadership and mission stakeholders on Data Science opportunities, analytical findings, technical approaches, and recommendations.
- Serve as a trusted technical advisor on the application of Data Science and AI to customer data and mission requirements.
- Ensure all solutions and activities comply with applicable security, classification, data governance, privacy, and customer policies.
Qualifications
Required Qualifications
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a related technical discipline; equivalent experience may be considered.
- 12+ years of relevant experience in Data Science, advanced analytics, machine learning, artificial intelligence, statistics, or a related discipline; Minimum of 10 years with MS/MA; Minimum of 7 years with Ph.D.
- Active TS or Q clearance or higher is required. Ability to obtain Q/SCI.
- Demonstrated experience analyzing and interpreting complex datasets to produce actionable insights.
- Experience developing and implementing Data Science, statistical, or machine learning models.
- Strong programming experience with Python, R, or equivalent analytical programming languages.
- Strong SQL and experience working with structured and/or unstructured data.
- Experience applying statistical analysis, predictive modeling, machine learning, or other advanced analytical techniques.
- Demonstrated experience translating mission, business, or operational requirements into analytical and technical solutions.
- Experience working with complex enterprise data environments and disparate data sources.
- Experience integrating analytical or AI/ML solutions with data platforms, applications, pipelines, or other technical environments.
- Understanding of data management principles, including data quality, metadata, data governance, data lineage, data interoperability, and data sharing.
- Demonstrated ability to work across multiple organizations and collaborate with diverse technical and mission stakeholders.
- Strong problem-solving, communication, presentation, and technical writing skills.
- Ability to work independently as an SME while effectively collaborating within multidisciplinary teams.
Preferred Qualifications
- Previous experience supporting the Intelligence Community, Department of Energy, or other U.S. Government mission environments.
- Experience supporting a government CAIO, AI, or enterprise Data Science organization.
- Experience identifying and evaluating AI/ML use cases within enterprise or mission environments.
- Experience integrating AI/ML solutions with existing government data assets and enterprise data platforms.
- Experience with generative AI, large language models, natural language processing, computer vision, knowledge graphs, or other advanced AI capabilities.
- Experience with cloud-based Data Science and AI environments, including AWS, Azure, or other government cloud platforms.
Details
Target Salary Range: $146,000 - $234,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Benefits Statement: Peraton offers eligible employees a variety of benefits including medical, dental, vision, life, health savings account, short/long term disability, EAP, parental leave, 401(k), paid time off (PTO) for vacation, and company paid holidays. A full listing of available benefits can be viewed at https://www.careers.peraton.com/benefits.
Application Statements: The application period for the job is estimated to be 30 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. By applying to this job, you are expressing interest in the role and the Company. During the review of your application, you may be required to participate in an on-camera interview, as well as participate in a process to verify your identity. Use of artificial intelligence (AI) tools of any kind during Peraton interviews is strictly prohibited unless the candidate has obtained prior written authorization. All interview responses must be the candidate’s own.
EEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.
Salary Context
This $146K-$234K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Peraton, 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. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $146K to $234K.
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.
Peraton AI Hiring
Peraton has 13 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer. Positions span Washington, DC, US, Reston, VA, Laurel, MD. Compensation range: $166K - $282K.
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.
Frequently Asked Questions
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