Interested in this Data Scientist role at Peraton?
Apply Now →Skills & Technologies
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
Peraton is seeking an experience Lead Data Scientist to join our team of qualified and diverse individuals. This position supports our customer as part of an application transformation and modernization initiative. This program is driving a large-scale transformation of systems into a data-centric, cloud-native ecosystem capable of supporting high-volume, near real-time data processing and advanced analytics. The work includes modernization of legacy applications, development of new cloud-native solutions, and implementation of DevSecOps and scaled Agile practices across the organization.
As a Lead Data Scientist at Peraton, you will define and drive enterprise data science and AI/ML strategy across a large-scale federal modernization program.
You will lead efforts spanning advanced analytics, machine learning, MLOps, AI governance, and operational analytics integration across complex enterprise systems. This role requires close collaboration with data engineering, architecture, application development, and operational teams to ensure AI/ML capabilities are production-ready, scalable, explainable, and integrated into enterprise workflows.
You will operate at both strategic and hands-on levels guiding technical direction, developing advanced models, and ensuring analytics solutions deliver measurable mission impact.
Location: Suitland, MD (Hybrid)
Day to Day Roles and Responsibilities
- Provide technical leadership across enterprise data science and AI/ML initiatives within a large-scale modernization program
- Design, develop, validate, deploy, monitor, and scale machine learning and advanced analytics solutions in production environments
- Lead implementation of MLOps practices supporting model lifecycle management, automation, observability, and continuous improvement
- Apply advanced data science techniques including NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analysis
- Design and support event-driven analytics and real-time/streaming ML pipelines
- Collaborate with data engineers, architects, application teams, and SMEs to integrate AI/ML capabilities into enterprise systems and operational workflows
- Support system-of-systems (SoS) integrations across multiple systems, vendors, contractors, and interdependent platforms
- Establish AI governance frameworks supporting fairness, bias mitigation, explainability, transparency, and compliance with standards such as the NIST AI Risk Management Framework
- Develop reproducible analytics workflows, technical documentation, analysis plans, dashboards, and reporting deliverables
- Support DataOps and Agile data science practices including iterative development, pipeline automation, CI/CD integration, and collaborative model delivery
- Ensure analytics solutions align with enterprise security, privacy, and compliance requirements
- Drive improvements in data quality, validation, accessibility, and operational analytics reliability
- Present findings, recommendations, and technical approaches to executive leadership and stakeholders
- Mentor data scientists and analytics teams while promoting best practices across the organization
Qualifications
Basic Qualifications:
- Bachelors degree and 16 years of experience or an Associates degree and 18 years of experience or a High School diploma/equivalent and 20 years of experience.
- Must be a U.S. Citizen with the ability to obtain a Public Trust clearance
- 15+ years of experience in data science, AI/ML, advanced analytics, or enterprise modernization initiatives
- Proven experience leading AI/ML and analytics efforts across large-scale, complex systems
- Deep expertise in machine learning, statistical modeling, and advanced analytics techniques
- Experience implementing end-to-end MLOps pipelines including model training, validation, deployment, monitoring, and scaling
- Experience with NLP, LLMs, deep learning, reinforcement learning, anomaly detection, and time series analytics
- Experience designing and supporting real-time or streaming analytics architectures
- Experience integrating AI/ML solutions across system-of-systems (SoS) environments and distributed enterprise platforms
- Experience implementing AI governance frameworks addressing explainability, fairness, transparency, and bias mitigation
- Experience with large-scale distributed data environments and cloud-native analytics platforms
- Experience with DataOps, CI/CD integration, and Agile/SAFe delivery models
- Strong experience with Python, R, Spark, TensorFlow, PyTorch, Databricks, and related AI/ML technologies
- Experience developing dashboards, technical reports, analysis plans, and reproducible analytics workflows
- Experience collaborating across engineering, architecture, application, and operational teams at enterprise scale
Preferred Qualifications:
- PhD is highly preferred in related technical field
- Experience supporting statistical and similarly large-scale federal modernization programs
- Experience implementing enterprise AI governance or responsible AI initiatives
- Experience with event-driven architectures, streaming analytics, or operational AI systems
- Experience supporting large-scale data modernization or enterprise analytics transformation efforts
- Experience working within DevSecOps-enabled AI/ML delivery environments
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 median 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 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 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $146K to $234K.
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.
Peraton AI Hiring
Peraton has 16 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Research Scientist, Data Scientist. Positions span Albany, NY, US, Laurel, MD, US, College Park, MD, US. Compensation range: $128K - $304K.
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.