AI Product Manager

Responsibilities

• Owning the complete post-purchase journey from voucher delivery through redemption and repeat engagement, with every metric in that funnel yours to move
• Building AI-driven features - personalised post-purchase communications, automated refund prevention, predictive churn signals - and taking them from concept to live production yourself, not handing off to a separate AI team
• Using AI tools in your daily product work: research synthesis, prototype generation, experiment design, data analysis - applied before engineering is involved, not as a finishing touch
• Designing and running A/B tests with documented hypotheses and instrumentation confirmed before launch, so every experiment produces a learning, not just a result
• Defining and tracking repeat purchase rate, time-to-next-buy, redemption success, and refund costs, and building the roadmap that moves them
• Maintaining consistency across web and mobile so the post-purchase experience doesn't fragment by platform
• What success looks like
• Day 30: Post-purchase funnel fully audited. Top three metric levers identified with supporting data. You can name the biggest AI leverage point in the funnel that nobody has acted on yet.
• Day 60: First AI-driven or A/B experiment live with a clear hypothesis and instrumentation confirmed before launch. You can explain the current repeat purchase rate and the leading indicators that predict it.
• Day 90: First experiment results in. If it moved the metric, you know why. If it didn't, you know why too. Roadmap for the next two quarters agreed with stakeholders, with AI features as a primary lever.
• What this role demands
• Daily, hands-on use of AI tools in product work : you can walk through a real example from the past week - what you built, what tool you used, and what it produced. "I use it for writing" does not meet the bar
• Production experience shipping AI or automation features: you understand the gap between a working demo and a live system, and you have closed it
• Hands-on A/B testing, funnel optimisation, and cohort analysis - you have designed the experiments and instrumented them yourself, not reviewed someone else's reports
• Consumer lifecycle economics: you speak the language of repeat rate, LTV, redemption drop-off, and refund cost, not just feature outcomes
• The ability to defend a commercially ambiguous prioritisation call to engineering, design, and commercial leadership with data and a clear point of view
• How we operate
• Extreme Ownership: Repeat rates, refund costs, redemption success - your name is on all of it. One owner, direct accountability, no handoffs.
• Speed Over Comfort: Your first AI-assisted prototype ships before your roadmap is finalised. A working experiment beats a perfect plan every time.
• Impact Obsessed: AI features that don't move the post-purchase metrics don't count. Build what's measurable, measure what matters.
• #LI-KH1 #LI-Hybrid #marci

Additional Information

Groupon connects 42 million customers with local experiences - restaurants, events, wellness and travel - and the million-plus merchants who deliver them. Our mission is to get people offline and into the real world at prices that make it possible. We're an AI-native company in the middle of a platform transformation, moving from a deals marketplace to an experience discovery platform that works for customers and merchants at the same time. Groupon's post-purchase funnel - voucher delivery, redemption, repeat engagement - is a direct lever on repeat purchase rate, lifetime value, and refund costs. This role owns that entire funnel with a mandate to move those numbers, using AI as the primary method of doing so. When this hire is working well, the metrics shift in a way the business can measure quarter over quarter. Who you'll work with Report to Jason Wong, Group Product Manager, Customer Experience & Trust. You'll work directly with engineers and designers across web and mobile. One honest signal: this is a high-ownership, low-handholding role . You define the metrics, run the experiments, and make the calls. If you need consensus before moving, the pace here will be uncomfortable.

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