Note: The job is a remote job and is open to candidates in USA. Leidos is seeking an Applied Data Scientist to support a large, mission-critical Navy program through its Performance Management team. The role applies data science and AI to operational datasets to identify performance improvement opportunities, improve POAM effectiveness, and enable proactive, code-first performance management.
Responsibilities
- 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
Skills
- 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
- 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
Benefits
- Health and Wellness programs
- Income Protection
- Paid Leave
- Retirement
- Remote work
Company Overview