Retention Product Manager, Thesis

• Experience spanning lifecycle marketing and product management — you've owned a product surface or shipped product work, not just campaigns
• A demonstrated AI-native working style: you build with AI tools daily, design workflows around them, and know where they fail
• Technical fluency: APIs, webhooks, integrations, automation platforms, and basic data work. You don't need engineering for everything
• Solid Klaviyo (or equivalent ESP) skills — enough to execute and to collaborate deeply with lifecycle marketing
• Strong experimentation instincts: hypothesis, test design, measurement, honest readouts
• High ownership and clear judgment about what moves retention versus what only creates activity

Desirable

• We are hiring an AI-first Retention Product Manager to own the customer's post-purchase product experience and the operating systems behind our retention programs
• This includes app and portal onboarding, subscription management, post-purchase education, loyalty and referral programs, and the AI-enabled production and QA workflows that power our lifecycle marketing
• This is a hybrid role for someone with strong lifecycle fundamentals who has grown into product thinking: defining requirements, shipping improvements with engineering, instrumenting outcomes, and treating AI as a core capability rather than a tool they occasionally use. You can build a Klaviyo flow yourself — but you'd rather build the system that produces a hundred of them reliably
• Own retention-impacting product surfaces: app and portal onboarding, subscription management, account experience, and post-purchase education
• Design, build, and operate AI-enabled production systems for retention — briefing, email/flow build, QA, research, and reporting workflows
• Translate retention strategy into product requirements and ship improvements in partnership with product, engineering, and design
• Run experiments that span product and lifecycle touchpoints: in-app prompts paired with email/SMS journeys, cancellation-save experiences, education sequences
• Build and operate loyalty, referral, VIP, and advocacy programs — platform selection, integration, and lifecycle activation
• Execute hands-on in Klaviyo when needed: flows, segmentation, QA
• Instrument retention product work so impact is measurable: event tracking, dashboards, experiment readouts
• Document systems and processes so workflows scale beyond you
• What great looks like:
• Retention product surfaces have a clear owner, roadmap, and measurable improvement cadence
• AI-enabled production systems run reliably with documented QA — operating infrastructure, not experiments
• Loyalty and referral move from idea to tested program without new headcount
• Product and lifecycle stop being separate lanes: experiments routinely span both