Product Manager, Lab

📋 Description

• • Join Fundraise Up, a rapidly growing global fundraising platform dedicated to empowering nonprofits by delivering fast, seamless, and high-converting donation experiences. Our innovative technology processes tens of millions of dollars in donations monthly for impactful organizations across North America, Europe, the UK, and Australia. We are renowned for our commitment to product excellence, performance, and a profound respect for both donors and the nonprofits we serve.
• • As Fundraise Up scales its core platform, we are strategically investing in high-risk, high-upside product initiatives poised to redefine the future of digital fundraising. This is where the 'Lab' comes into play – a dedicated space for exploration and innovation.
• • As a Product Manager for the Lab, you will be at the forefront of exploring, validating, and de-risking bold, high-uncertainty product opportunities. These initiatives may not always appear as direct extensions of Fundraise Up's current offerings, pushing the boundaries of what's possible in the nonprofit sector.
• • Your primary objective is to accelerate learning and reduce uncertainty, rather than simply shipping features. You will own ideas from inception to decision, guiding them through early exploration, hypothesis framing, rapid experimentation, and ultimately, to clear investment choices: scale, pivot, or kill. The expectation is that most ideas will be terminated early, while a select few, backed by strong evidence, may graduate to our New Markets or Core product teams.
• • Success in this role is measured by the speed of learning and the quality of decision-making, not by the volume of output or traditional adoption metrics.
• • **What You'll Do:**
• • **Explore High-Risk, Technology-Driven Opportunities:** Identify emerging opportunities driven by new technologies, AI advancements, data insights, evolving platform capabilities, or significant infrastructure shifts. Translate nascent signals and technical possibilities into well-defined product hypotheses. Explore nascent ideas even before a clear buyer, established category, or definitive demand signal emerges. Maintain a dynamic exploration backlog, meticulously documenting risks, assumptions, and critical learning objectives.
• • **Design Experiments & Define Kill Criteria:** Frame experiments around the single riskiest assumption to maximize learning efficiency. Define explicit, measurable kill criteria *before* commencing any development work. Select the appropriate fidelity for experiments, ranging from low-fidelity prototypes and technical spikes to Wizard-of-Oz tests or live pilots. Build only what is absolutely necessary to achieve the learning objective – never more.
• • **Run Fast Experiments & Pilots:** Execute scrappy prototypes, Minimum Viable Products (MVPs), and pilots with tightly controlled scope to accelerate learning. Collaborate closely with Engineering, Design, Data, and Go-to-Market (GTM) teams throughout the experimentation process. Ruthlessly protect the pace of learning and actively avoid premature optimization.
• • **Make Clear Investment Decisions:** Synthesize experimental results into clear, opinionated recommendations: Scale, Iterate, or Kill. Communicate findings effectively, articulating precisely what was tested, what was learned, and what critical questions remain unanswered. Prevent the continuation of unproductive initiatives by ensuring every experiment concludes with a definitive decision. Be prepared to advocate for the termination of your own ideas when the evidence suggests they are not viable.
• • **Prepare Clean Escalation & Handoffs:** When an opportunity demonstrates strong potential, meticulously prepare it for handoff to other teams. This includes presenting validated value and problem hypotheses, comprehensive evidence from experiments or pilots, clearly defined risks, assumptions, and success metrics, and a proposed ownership and scaling model. Ensure a complete transfer of ownership, as the Lab's mandate is exploration, not the management of scaled products.
• • **Operate Transparently & Share Learnings:** Maintain a visible Lab portfolio showcasing ongoing explorations, the rationale behind them, and current findings. Publish decision memos and learning summaries to foster transparency. Openly share learnings from failed experiments when they provide clear insights.
• • **Leverage AI to Accelerate Learning:** Utilize cutting-edge AI tools to expedite research, synthesis, prototyping, and experimentation processes. Explore AI-enabled product ideas with a pragmatic perspective, considering factors like cost, latency, data requirements, and accuracy. Critically distinguish between genuine capability shifts and market hype. Help the organization understand when AI genuinely accelerates learning and when it does not.
• • **Launch High-Risk, High-Signal Initiatives:** Selectively launch bold initiatives, even when short-term adoption is uncertain. Treat these launches as genuine product bets, not mere demonstrations. Use these launches to test future market categories, shape market perception, and signal technical leadership. Be explicit about the intent behind each launch: whether it's for learning, creating optionality, or external signaling.