Product Manager, MongoDB

• 3–5 years of experience in product management or adjacent roles in IT, data, or software (internships, apprenticeships, research projects, or relevant full-time experience all count)
• Familiarity with AI/ML concepts and workflows (LLMs, agents, copilots, prompt design, evaluation) and an eagerness to learn how to apply them to internal enterprise use cases
• Strong fundamentals in PM craft, including writing concise PRDs/requirements, defining acceptance criteria, running lightweight discovery, and partnering with engineering
• Clear communication and collaboration skills with the ability to translate technical details into user value and to coordinate across multiple stakeholders
• Comfort with data and measurement (basic experimentation concepts, funnels, dashboards) to track adoption and outcomes
• Awareness of responsible AI practices (data privacy, security, model limitations, bias) and willingness to follow established guardrails
• Bachelor’s degree in Computer Science, Information Systems, Data/Analytics, or related field, or equivalent practical experience

Desirable

• We are seeking a highly motivated IT Product Manager for Internal AI to help deliver and scale AI-powered solutions across our enterprise IT landscape
• You’ll support the product lifecycle for internal AI tools; partnering with senior PMs, engineers, and operational teams to turn defined opportunities into shipped capabilities that improve service delivery, knowledge management, automation, and decision support
• This is a hands-on role for someone early in their product career who is excited to learn, work cross-functionally, and make measurable impact
• Product execution and delivery:
• Support the end-to-end lifecycle for internal AI products; from discovery tasks, writing clear requirements, coordinating prototypes, to assisting with launches and post-launch iteration under guidance from senior PMs
• Backlog and roadmap support:
• Maintain clean user stories, acceptance criteria, and prioritization for a scoped backlog; contribute to release notes and lightweight roadmap updates
• User research and requirements:
• Participate in interviews, help map critical user journeys, synthesize insights, and translate them into problem statements and MVP scopes
• Adoption and enablement:
• Assist with UAT, documentation, training materials, and rollout plans that drive adoption and measurable outcomes (time saved, CSAT, deflection)
• Analytics and outcomes:
• Partner with engineering/analytics to define success metrics, instrument basic telemetry, and report on feature usage and impact
• Stakeholder collaboration:
• Work with IT partners (Applications, Infrastructure, Security, Service Desk) and business stakeholders to clarify needs, gather feedback, and keep work unblocked
• Governance and responsibility:
• Learn and apply responsible AI practices and guardrails in partnership with data, security, and compliance teams
• AI evangelism:
• Contribute to internal share-outs, demos, and office hours to build awareness of what’s shipped and what’s next
• Success Measures:
• Delivery of scoped, high-quality features that address clearly defined user problems
• Demonstrated improvement in adoption and user satisfaction for assigned capabilities
• Clear, reliable PM artifacts (requirements, user stories, release notes) and on-time execution
• Positive feedback from stakeholders on collaboration, clarity, and follow-through