Principal Data Scientist

Description

The Leidos Chief Data & Analytics Office (CDAO) is a new, high-growth organization at the center of the company's technology strategy. Our Operational AI (Ops.

AI) division is seeking an experienced and innovative Principal Data Scientist to help drive the delivery of high-impact data science solutions. This role is essential for translating complex business challenges into data-driven insights that improve mission-critical outcomes. This is an exciting opportunity to leverage your experience to solve complex problems and optimize decision-making in dynamic environments.

As a Principal Data Scientist, you will be a key technical contributor, applying your analytical expertise to develop and deploy advanced analytical models and AI solutions. You will work closely with domain experts and other data scientists to ensure our projects are technically sound, strategically aligned, and deliver measurable value to our nation's most pressing operational challenges.

• End-to-End Model Development:
  Lead the technical execution of data science projects, from problem formulation and data exploration to model development, validation, and deployment.
• Advanced Analytics Application:
  Apply advanced techniques, including machine learning, predictive modeling, and statistical analysis, to extract actionable insights from large and complex datasets.
• Stakeholder
  
  Collaboration:
  
  Work closely with stakeholders and domain experts to understand business needs, define technical requirements, and translate them into data science tasks.
• Technical Mentorship:
  Provide technical guidance and mentorship to junior data scientists on advanced analytics, machine learning techniques, and best practices.
• Quality & Rigor:
  Ensure the quality, rigor, and scalability of data science solutions, adhering to trusted and responsible AI principles.
• Insight Communication:
  Communicate complex analytical findings and recommendations clearly to both technical and non-technical audiences to drive data-driven decision-making.
• Innovation:
  Stay current with the latest advancements in data science and AI & ML, and champion their adoption to solve new and challenging problems.

• A Bachelor's degree in a quantitative field such as Data Science, Statistics, Computer Science, or a related discipline with 12+ years of experience as a data scientist, OR a Master's degree with 10+ years of relevant experience.
• Proven experience taking data science projects or significant project modules from conception to deployment.
• Strong proficiency in programming languages such as Python or R and extensive experience with common data science libraries (e.g., pandas, scikit-learn, Tensor Flow, PyTorch).
• A solid foundation and demonstrated expertise in machine learning, statistical modeling, and experimental design.
• Must be a U.S. Citizen and able to obtain and maintain a security clearance.

• Experience in the defense, intelligence, or federal civilian sectors.
• Experience working with large-scale or unstructured datasets (e.g., text, imagery).
• Knowledge of Generative AI, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures.
• Familiarity with big data technologies (e.g., Spark, Hadoop) and cloud-based data science platforms (e.g., AWS Sage Maker, Azure ML).
• Experience with data visualization tools like Tableau or Power BI to communicate findings.
• A portfolio of completed data science projects or contributions to open-source projects.

At Leidos, we don’t want someone who "fits the mold"—we want someone who melts it down and builds something better. This is a role for the restless, the over-caffeinated, the ones who ask, “what’s next?” before the dust settles on “what’s now.”

If you’re already scheming step 20 while everyone else is still debating step 2… good. You’ll fit right in.

January 6, 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.