Applied Data Scientist – Performance Management & AI Solutions

Leidos

1d•$87,100 - $157,450•Remote

About The Position

Leidos is seeking an Applied Data Scientist to join their Performance Management team, supporting a large, mission-critical Navy program. This role is focused on using data, AI, and software development to solve operational performance challenges, build analytical capabilities, and deliver measurable improvements. The ideal candidate will combine strong technical development skills with an analytical and consultative mindset to understand operational challenges, identify opportunities, develop and implement solutions, and ensure those solutions deliver value. The work involves Service Level Requirements (SLRs) and broader Navy performance initiatives like World Class Alignment Metrics (WAM). Key priorities include identifying performance improvement opportunities, strengthening Plans of Action and Milestones (POAMs), and developing AI-enabled tools for proactive performance management. This position is remote.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.