Product Manager

Responsibilities

The Product Manager is a core member of the Data & AI Product Innovation (DAPI) Product Discovery team, responsible for owning the full product lifecycle for an assigned domain of data and AI products. Reporting to the Director, Product Discovery, this role leads continuous discovery, defines requirements, drives stakeholder alignment, and ensures that engineering investment is directed at problems worth solving.

 

DAPI operates as a lean, high-performing team supporting multiple product domains simultaneously. This means each Product Manager owns a cluster of related products — not a single application — and must be capable of managing competing priorities, rapidly shifting context, and making sound product decisions with incomplete information.

 

This role requires strong analytical self-sufficiency. You will define what success looks like for your products, partner with Analytics Engineers and Data Scientists to measure it, and use data to drive your own prioritization and discovery decisions. You will work with DAPI's embedded Analytics Engineering and Data Science teams to define, measure, and interpret product outcomes — bringing the domain context and analytical direction that makes that collaboration effective.

 

You will work closely with all users from all business verticals, data and software engineering teams, data science and AI teams, as well as senior leadership — applying design thinking principles to uncover real user needs and translate them into product solutions. You are the voice of the user within the DAPI team and the voice of the team to the business.

 

Product Discovery & Validation

• Lead continuous discovery for your product domain — problem framing, assumption mapping, user interviews, and rapid validation.
• Reduce risk across four dimensions before engineering investment: value, usability, feasibility, and viability.
• Engage directly with business users to uncover unmet needs, workflow friction, and high-value opportunities.
• Translate ambiguous business problems into well-scoped, testable product hypotheses.
• Validate product concepts through lightweight prototypes, storyboards, or data experiments before committing to build.

Product Strategy & Roadmap Ownership

• Own the product roadmap for your assigned domain — balancing quick wins, strategic investments, and technical debt.
• Prioritize using evidence from users, data, and business goals — defend prioritization decisions clearly.
• Define and communicate outcome-focused product goals that align with DAPI’s strategic objectives.
• Maintain a living backlog that reflects current understanding, not just accumulated requests.

Requirements & Delivery Partnership

• Author clear, well-structured product requirements, user stories, and acceptance criteria.
• Partner with Engineering and Architecture throughout delivery to resolve ambiguity, make trade-off decisions, and keep work moving.
• Participate actively in Agile/SCRUM ceremonies — sprint planning, backlog refinement, reviews, and retrospectives.
• Work closely with Delivery Management to track progress, surface risks, and communicate status to stakeholders.
• Validate delivered features against acceptance criteria and user intent before release. 

AI Product Ownership

• Lead discovery and requirements definition for AI and machine learning product features within your domain.
• Understand when AI is the right solution and when it is not.
• Define human-in-the-loop requirements, explainability expectations, and trust signals for AI-powered features.
• Partner with Data Science to translate model capabilities into user-centered product experiences.
• Design for AI-specific risks: bias, uncertainty, user over-reliance, and failure modes.

Analytics & Measurement

• Define success metrics and KPIs for every product initiative before development begins.
• Partner with Analytics Engineers to instrument features, build product health dashboards, and monitor adoption.
• Conduct post-go-live analyses to assess whether shipped products achieved intended outcomes.
• Use data independently to interrogate product performance and form your own hypotheses.
• Proactively identify underperformance or emerging user needs through ongoing monitoring.

Stakeholder Engagement & Communication

• Build strong working relationships with stakeholders across all divisions.
• Communicate product decisions, trade-offs, and outcomes clearly to both technical and non-technical audiences.
• Manage stakeholder expectations