Applied Data Scientist - Performance Management & AI Solutions

Leidos is seeking an Applied Data Scientist to join our Performance Management team supporting a large, mission-critical Navy program. This is a hands-on, solution-oriented role focused on using data, AI, and software development to solve operational performance challenges, build practical analytical capabilities, and deliver measurable improvements.

We are looking for someone who enjoys turning complex, sometimes ambiguous problems into working solutions. The ideal candidate combines strong technical development skills with an analytical, consultative mindset—someone who can understand operational challenges, identify opportunities, develop and implement solutions, and work with stakeholders to ensure those solutions deliver value.

The work centers on Service Level Requirements (SLRs) that govern contractual performance across the program, with additional support to broader Navy performance initiatives such as World Class Alignment Metrics (WAM). Key priorities include identifying performance improvement opportunities, strengthening the effectiveness of Plans of Action and Milestones (POAMs), and developing AI-enabled tools and workflows that help the team move from reactive analysis to proactive performance management.

This role sits at the intersection of applied analytics, AI solution development, and operational performance consulting. You will work closely with analysts, engineers, and performance leaders to translate business needs into practical technical solutions, while helping expand the team's ability to use AI, automation, and reusable code in everyday operations.

This position is remote.

Please be advised that during the interview, you will be required to keep your camera on, and your interviewer will be taking your picture for identification purposes if an offer letter is extended to you.

What You'll Do

• Develop and deliver practical solutions using Python, AI, data analytics, and automation to address real-world operational performance challenges.
• Partner with performance analysts, engineers, and program stakeholders to understand business problems, define requirements, recommend approaches, and translate opportunities into actionable technical solutions.
• Build, test, refine, and implement reusable code, analytical tools, and AI-enabled workflows that improve how the team identifies, investigates, and addresses performance issues.
• Analyze complex operational data to identify emerging risks, recurring issues, root causes, and opportunities to improve contractual performance.
• Apply AI and machine learning where appropriate to accelerate analysis, identify patterns, support decision-making, and improve the prioritization and effectiveness of POAMs.
• Develop prototypes and proof-of-concept solutions, then work with the team to turn successful approaches into repeatable, sustainable capabilities.
• Improve existing analytical processes through automation, integration of data sources, and more efficient code-first workflows.
• Translate analytical findings into clear recommendations, practical actions, and measurable performance improvements.
• Communicate effectively with technical and non-technical stakeholders, explaining not only what the data shows but what actions should be taken and why.
• Serve as a hands-on resource for AI adoption and capability development, helping teammates identify useful applications and incorporate new tools and techniques into their work.
• Support broader Navy performance initiatives by adapting and extending successful solutions across related operational challenges.

Required Qualifications

• Bachelor's degree and 4–8 years of prior relevant experience, or Master's degree with 2–6 years of prior relevant experience. Additional years of experience may be accepted in lieu of a degree.
• US citizenship
• Must have an active DoD secret security clearance.
• Demonstrated experience developing and implementing data-driven, analytical, automation, or AI-enabled solutions to solve real-world business or operational problems.
• Strong Python programming skills, including experience building reusable scripts, analytical workflows, tools, or applications.
• Experience working directly with stakeholders to understand business needs, frame problems, define requirements, and deliver practical solutions.
• Experience using data analysis, applied machine learning, or AI techniques to generate actionable insights and support operational decisions.
• Ability to work with large, complex, or imperfect datasets and transform them into reliable, usable analytical outputs.
• Strong problem-solving skills and the ability to move independently from problem identification through solution development and implementation.
• Ability to communicate technical concepts, findings, and recommendations clearly to both technical and non-technical audiences.
• Collaborative, results-oriented mindset with a willingness to iterate, incorporate feedback, and adapt solutions to operational needs.

Preferred Qualifications

• Experience developing AI-enabled applications, automation solutions, or decision-support tools using Python and modern development frameworks.
• Hands-on experience with generative AI, large language models (LLMs), AI APIs, or related tools to solve business problems or improve operational workflows.
• Experience with SQL, data pipelines, APIs, or integrating data from multiple systems.
• Familiarity with software development practices such as Git, version control, testing, documentation, and reusable code design.
• Experience in a technical consulting, analytics consulting, solutions engineering, or business-facing development role.
• Experience developing prototypes, demonstrating solutions to stakeholders, and iterating based on user feedback.
• Familiarity with operational performance metrics, service-level agreements, continuous improvement, or contract-driven environments.
• Experience developing analytical solutions that support prioritization, root cause analysis, forecasting, or operational decision-making.
• Experience supporting large-scale enterprise or government environments.
• Ability to balance technical sophistication with practicality, maintainability, explainability, and user adoption.

What Success Looks Like

• Working solutions are delivered, not just analyses or recommendations, and those solutions make a measurable difference in operational performance.
• Performance improvement opportunities are identified earlier and translated into practical actions that help teams address underlying issues.
• POAMs are better prioritized, more actionable, and more effective at improving contractual performance.
• Repetitive analytical tasks are automated, allowing the team to spend more time solving problems and less time preparing data or producing reports.
• AI-enabled tools and workflows are implemented, adopted, and trusted by analysts, engineers, and performance leaders.
• Reusable code, tools, and analytical capabilities are developed that improve the team's effectiveness and can be extended to new use cases.
• Stakeholders view this role as a practical technical partner who understands their challenges, develops useful solutions, and helps deliver results.
• The team continues progressing toward a more proactive, code-first, AI-enabled performance management model.

NGEN

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:

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:

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.