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About This Role
Description
Turn Mission Data into Mission Advantage.
Are you ready to turn complex data into insights that drive mission-critical decisions? We are looking for an exceptional Data Scientist Subject Matter Expert (SME) to join the Chinook Program and help shape the future of an advanced application supporting an Intelligence Community (IC) customer.
This is an exciting opportunity for a highly accomplished data science leader who thrives on solving difficult problems, discovering patterns hidden within complex datasets, and transforming analytical insights into operational capabilities. You will work at the intersection of data science, software engineering, artificial intelligence, machine learning, analytics, and mission operations to develop innovative solutions that make a measurable difference.
As a Data Scientist SME, you will lead the design, development, evaluation, and operationalization of sophisticated data science capabilities. The role requires a combination of deep technical expertise, analytical rigor, software engineering understanding, and the ability to translate complex mission requirements into actionable data-driven solutions.
The successful candidate will work closely with software engineers, data engineers, system engineers, DevSecOps engineers, UI/UX professionals, product owners, test engineers, program leadership, and intel customer stakeholders.
The position requires an individual who can operate at both the strategic and hands-on levels establishing data science strategy while remaining technically engaged in solving complex analytical and engineering challenges.
Position may be performed in the following locations below.
- Tucson, AZ
- Gaithersburg
- Alexandria, VA
- Chantilly, VA
- Englewood, CO
- St. Louis, MO
Clearance Level Required:
Active Top Secret/SCI clearance with the ability to successfully pass a Polygraph examination.
Primary Responsibilities:
- Serve as the senior technical authority for data science, advanced analytics, and AI/ML, translating mission needs into actionable analytical solutions.
- Lead the design, development, validation, and optimization of statistical, predictive, prescriptive, and machine learning models.
- Analyze complex, heterogeneous datasets to identify patterns, anomalies, trends, and insights that support mission objectives.
- Drive the development and operationalization of AI/ML capabilities, including classification, regression, clustering, anomaly detection, NLP, and predictive modeling.
- Establish standards and best practices for model performance, validation, explainability, bias, uncertainty, reproducibility, and operational effectiveness.
- Partner with data, software, and systems engineers to define data requirements, feature engineering, data quality, governance, and scalable integration approaches.
- Collaborate with engineering and DevSecOps teams to transition analytical solutions into secure, maintainable, scalable production capabilities and CI/CD pipelines.
- Support Agile and SAFe planning and execution by translating data science initiatives into capabilities, stories, technical enablers, dependencies, and risks.
- Lead technical reviews, trade studies, and working sessions, and establish methodologies, patterns, and practices across the program.
- Engage with customers, stakeholders, and leadership to communicate findings, recommendations, risks, and mission impact.
- Mentor data scientists, engineers, and other technical team members, and review analytical approaches, code, models, and documentation.
- Evaluate and recommend emerging data science and AI/ML technologies to improve application capabilities and mission outcomes.
Basic Qualifications:
- US citizenship is required per contract
- Bachelor’s degree in Data Science, Computer Science, Geospatial Science or related field and 12-15 years of prior relevant experience or Master’s with 10-13 years of prior relevant experience. May possess a Doctorate in technical domain.
- 12+ years demonstrated experience working as a Data Scientist, Senior/Principal Data Scientist, Machine Learning Engineer, or equivalent senior technical role.
- Extensive experience applying statistical analysis, machine learning, and advanced analytics to complex problems.
- Proven expertise in developing and implementing machine learning and AI models, particularly in context of geospatial or remote sensing.
- Deep knowledge of geospatial analytics tools (e.g., GIS, ArcGIS), remote sensing techniques, and the application of data science in the IC.
- Expert proficiency in Python and/or R (or similar languages) and experience with data science libraries (Pandas, NumPy, SciPy, Scikit-learn, TensorFlow, PyTorch, or equivalent tecchnologies).
- Strong experience with big data processing tools (e.g., Spark, PySpark, Kafka, Hadoop, AWS or Azure cloud platforms).
- Expertise in working with geospatial data formats (e.g., GeoTIFF, Shapefiles, WMS, WFS) and spatial libraries (e.g., GeoPandas, Rasterio, GDAL).
- Advance experience in developing and operationalizing AI/ML models and algorithms for geospatial data (e.g., object detection from satellite imagery, spatial clustering, predictive analytics).
- Strong background in data visualization and reporting tools (e.g., Tableau, PowerBI).
- Strong leadership, communication, and collaboration skills, with the ability to work directly with senior government officials, analysts, and technical teams.
- Excellent problem-solving and analytical skills with a demonstrated ability to translate technical challenges into actionable insights.
Preferred Qualifications:
- Advanced certifications in data science or machine learning.
- Familiarity with the customer’s mission and specific geospatial intelligence challenges.
- Expertise in advanced deep learning techniques, particularly in computer vision and image analysis for geospatial applications.
- Experience with cloud-native applications and distributed computing in a geospatial context.
- Experience deploying models into production environments.
- Experience with Kubernetes and containerized applications.
- Experience developing REST APIs or other services for analytic capabilities.
- Experience with NLP and/or LLM.
- Experience with geospatial analytics and spatial data.
- Experience with streaming or near-real time analytics.
- Experience with developing analytical capabilities within secure or air-gapped environments.
- Published research or technical papers in geospatial intelligence, machine learning, or data science.
#Chinook
#NSBA
If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.
Original Posting:
October 15, 2026
For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:
Pay Range $131,300.00 - $237,350.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
About Leidos
Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit www.Leidos.com.
Pay and Benefits
Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at www.leidos.com/careers/pay-benefits.
Securing Your Data
Beware of fake employment opportunities using Leidos’ name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the Leidos.com automated system – never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at [email protected].
If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission.
Commitment to Non-Discrimination
All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.
Salary Context
This $131K-$237K range is above the median for Data Scientist roles in our dataset (median: $162K across 195 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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Leidos, 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 1,564 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $131K to $237K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Leidos AI Hiring
Leidos has 4 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist. Positions span Reston, VA, US, Huntsville, AL, US, Chantilly, VA, US. Compensation range: $104K - $237K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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