Data Product Lead

The Leidos Digital Modernization Sector is seeking a Data Product Lead; this position will allow for full time telework from any U.S. based location.

POSITION SUMMARY:
The Data Product Lead will assist and drive the design, and delivery of high-value data products that enable advanced analytics, AI/ML, and agentic AI use cases. This role requires a product development mindset, with a strong ability to balance business priorities, user needs, and technical capabilities and will serve as the bridge between functional data product owners, business stakeholders, and technical teams (data engineering, data science, data architecture, and governance). They will be accountable for ensuring data products are well-defined, user-focused, and delivered with measurable impact on business outcomes. 

PRIMARY RESPONSIBILITIES: 

• Partner with functional team leadership to co-own the vision, roadmap, and success criteria for specific data products that support business operations, AI/ML, and agentic AI solutions. 
• Collaborate with functional data product owners to capture business needs and translate them into technical requirements. 
• Coordinate technical staff (data engineers, architects, data scientists, governance specialists) to deliver scalable, reliable, and AI-ready data products. 
• Ensure alignment of data products with enterprise data architecture, governance, and quality standards. 
• Partner with Data Scientists and AI Engineers to ensure data products support advanced ML, GenAI, and agentic AI use cases (e.g., enabling RAG pipelines, semantic search, or autonomous agents). 
• Apply a product development mindset, focusing on usability, reusability, adoption, and measurable business value. 
• Track and report on product performance, usage, and ROI, adjusting roadmaps as needed. 
• Act as a champion for data products, promoting awareness, adoption, and training across the organization. 
• Drive continuous improvement by incorporating feedback loops from business users and technical teams. 
• Contribute to the development of data product standards, documentation, and playbooks. 

BASIC QUALIFICATIONS:

• Bachelor’s degree in Data Science, Computer Science, Information Systems, Business, or related field, with 4+ years of relevant experience; additional years of experience may be substituted in lieu of a degree.  
• Experience in product management or product leadership, ideally with data or analytics platforms. 
• Understanding of data platforms and pipelines (e.g., Snowflake, Informatica, Immuta, Collibra, cloud data services). 
• Familiarity with AI/ML concepts and how data products enable advanced AI solutions. 
• Skilled in requirements gathering, backlog management, and stakeholder engagement. 
• Ability to coordinate cross-functional technical teams (engineering, architecture, governance, science). 
• Translate business needs into technical data product requirements. 
• Excellent communication 
• US citizenship required. 

PREFERRED QUALIFICATIONS:

• Experience with data mesh or data product-oriented architectures. 
• Exposure to Generative AI and agentic AI use cases, particularly how they leverage structured, unstructured, and semi-structured data. 
• Knowledge of data governance, compliance, and security principles. 
• Familiarity with agile product development practices and tools (Jira, Confluence, Azure DevOps). 
• Experience working in regulated industries (finance, healthcare, defense, government). 
• Certifications in product management, agile practices, or data platforms (e.g., Certified Scrum Product Owner, Pragmatic Institute, Snowflake, Collibra)

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.

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.