Leidos is seeking an Applied Data Scientist to join our Performance Management team supporting a large, mission-critical Navy program. This role is focused on using data science and AI to identify performance improvement opportunities, improve the effectiveness of plans of action and milestones (POAMs), and enable a more proactive, code-first approach to performance management.

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). The emphasis is on applied problem solving, not reporting-using AI and data science to surface patterns, leading indicators, and improvement opportunities that would not be found through traditional analysis.

This role sits at the intersection of data science, operational performance, and AI enablement. You will collaborate closely with analysts, engineers, and performance leaders, while also serving as a hub for AI-driven exploration and capability building across the team.

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

• Apply data science and AI techniques to large, complex operational datasets to identify performance degradation, systemic issues, and improvement opportunities.
• Design and implement code-first analyses and models that support automated or semi-automated performance opportunity discovery.
• Use AI and advanced analytics to evaluate, prioritize, and improve the effectiveness of POAMs tied to contractual performance outcomes.
• Work collaboratively with performance analysts, engineers, and stakeholders to translate operational problems into data-driven solutions.
• Explore and apply context-appropriate AI techniques, balancing innovation with explainability and defensibility.
• Build reusable analytical approaches, code, and methods that elevate the team’s overall capability in data science and AI.
• Communicate insights clearly and credibly to technical and non-technical stakeholders, including leadership.
• Support broader Navy performance initiatives (e.g., WAM) by extending methods and insights beyond SLR-focused use cases where appropriate.

Required Qualifications

• Bachelors and 4 - 8 years of prior relevant experience or Masters with 2 - 6 years of prior relevant experience. Additional years of experience may be accepted in lieu of a degree. 
• US citizenship with the ability to obtain and maintain an active DoD Secret security clearance.
• Experience applying data science, machine learning, or AI to real-world operational or performance problems.
• Strong problem-framing skills: ability to understand ambiguous problems, identify relevant data, and determine appropriate analytical approaches.
• Proficiency in Python or similar languages for data analysis and modeling.
• Experience working with large, messy, or heterogeneous datasets.
• Ability to explain complex analytical results clearly and defensibly.
• Collaborative mindset and comfort working as part of a multidisciplinary team.

Preferred Qualifications

• Experience with applied machine learning, statistical modeling, or AI techniques used for pattern detection, prioritization, or forecasting.
• Familiarity with operational performance metrics, service-level agreements, or contract-driven environments.
• Experience balancing advanced methods with explainability and stakeholder trust.
• Exposure to AI enablement, platform thinking, or reusable analytics frameworks.
• Experience supporting large-scale or enterprise environments.

What Success Looks Like

• Performance improvement opportunities are identified earlier, more consistently, and with greater confidence.
• POAMs are better prioritized and more effective, with clearer data-driven justification.
• AI-driven insights are adopted and trusted by the team and leadership.
• Reusable data science and AI capabilities exist that others on the team can build upon.
• The team increasingly operates in a 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:

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.