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Open nowPosted 8 days ago

Data Scientist SME

Leidos1,866 open roles

Pay
$131,300 – $237,350 a year
Where
Tucson AZ
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Your applicationOpen nowData Scientist SMELeidos · Tucson AZ

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The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. Leidos postings stay open a median of 7 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 8 days ago

Leidos median: 7 days open

The posting

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.

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