We are looking for a Product Manager to own a product workstream within our AI-powered productivity solutions for Japan's marketplace. You will be responsible for the end-to-end product outcome — not just coordination — from deeply understanding user workflows, to defining requirements, to driving delivery with engineering and data science teams, to measuring real-world impact after launch.
You will own your product workstream independently: making prioritization decisions, defining launch criteria, managing stakeholder alignment, and being accountable for the productivity gains your product delivers to Account Managers and Merchant Consultants.
The products you'll own leverage generative AI to automate manual processes, surface data-driven insights, and enable faster decision-making for sales teams. Success requires you to combine product management fundamentals (user research, requirements, delivery milestones) with the ability to evaluate AI output quality, design evaluation frameworks, and iterate based on quantitative evidence.
You are expected to proactively identify opportunities, drive accountability across cross-functional teams, and deliver results in ambiguous, fast-paced environments. Success means not just launching features, but validating that they work — measuring real productivity gains and continuously improving AI quality based on data.
Key job responsibilities
• Identify and validate critical user needs through direct conversations, workflow observation, and data analysis — not assumptions.
• Translate validated needs into prioritized requirements (P0/P1/P2) with clear acceptance criteria for engineering and data science teams.
• Own milestone-based delivery: requirements aligned → goals set → roadmap locked → launch criteria defined → testing complete → launch → impact measured.
• Define measurable evaluation metrics for AI-powered features — accuracy thresholds, error rates, user satisfaction baselines — as part of launch criteria, not as an afterthought.
• Design and maintain evaluation frameworks to continuously monitor and improve AI output quality post-launch.
• Redesign operational processes at scale, identifying where automation and AI can replace manual workflows.
• Coordinate user acceptance testing with real users and iterate based on quantitative feedback.
• Identify cross-product overlaps and coordinate with other PMs to avoid duplicate efforts and ensure coherent user experience.
• Escalate early and effectively when timelines slip or blockers persist — with analysis and proposed solutions.
• Measure post-launch impact against productivity targets (time saved, adoption rate, return rate) and demonstrate ROI with data.
• Communicate product progress, key decisions, and impact metrics to senior leadership through regular business reviews and written narratives.[
