Product Manager, Lab

📋 Description

• • Join Fundraise Up's innovative 'Lab' team as a Product Manager, tasked with exploring and validating high-risk, high-upside product opportunities that could redefine the future of digital fundraising.
• • This is a unique opportunity to operate at the forefront of innovation, focusing on discovery and de-risking rather than traditional feature shipping. Your primary objective will be to accelerate learning and ensure sound decision-making.
• • You will own the entire lifecycle of new ideas, from initial exploration and hypothesis generation to designing and executing rapid experiments, culminating in clear investment decisions: scale, pivot, or kill.
• • The Lab's mission is to systematically explore what's next for Fundraise Up, identifying new capabilities, emerging categories, and novel applications of technology within the fundraising landscape.
• • Your role involves translating nascent signals from new technologies, AI advancements, data insights, and evolving platform capabilities into actionable product hypotheses.
• • You will proactively explore promising ideas even in the absence of a clear buyer, established market category, or definitive demand signal.
• • Maintain a dynamic exploration backlog, meticulously documenting identified risks, critical assumptions, and defined learning objectives for each initiative.
• • Design experiments with a laser focus on testing the single riskiest assumption, ensuring maximum learning efficiency.
• • Define explicit 'kill criteria' before commencing any development, establishing clear thresholds for continuing or discontinuing an initiative.
• • Select the appropriate fidelity for experiments, ranging from low-fidelity prototypes and technical spikes to Wizard-of-Oz tests and live pilots, always prioritizing learning speed.
• • Adhere to the principle of building 'just enough' to validate hypotheses, rigorously avoiding premature optimization or scope creep.
• • Execute scrappy, lean experiments, Minimum Viable Products (MVPs), and pilots with tightly controlled scope to maximize learning velocity.
• • Collaborate closely with Engineering, Design, Data Science, and Go-to-Market teams throughout the experimental process, ensuring alignment and shared understanding.
• • Ruthlessly protect the pace of learning, actively resisting the temptation of premature optimization that can obscure true insights.
• • Synthesize experimental results into clear, opinionated recommendations: Scale, Iterate, or Kill, providing a decisive path forward.
• • Communicate findings effectively, articulating precisely what was tested, the key learnings derived, and any remaining unknowns.
• • Uphold a strict 'no zombie initiatives' policy, ensuring every experiment concludes with a definitive decision.
• • Demonstrate the courage to 'kill your own ideas' swiftly when the evidence suggests they are not viable.
• • When an opportunity demonstrates strong potential, meticulously prepare it for handoff to core or new market teams, providing validated value and problem hypotheses, robust experimental evidence, clearly defined risks and success metrics, and a proposed ownership and scaling model.
• • Ensure a complete and transparent transfer of ownership, as the Lab's mandate is exploration, not the management of scaled products.
• • Maintain a transparent Lab portfolio, clearly communicating ongoing explorations, their rationale, and current performance signals.
• • Publish decision memos and learning summaries to foster organizational knowledge sharing and transparency.
• • Freely share learnings from failed experiments, recognizing their value in guiding future efforts.
• • Leverage modern AI tools to accelerate research, data synthesis, prototyping, and experimental design, enhancing learning speed.
• • Critically evaluate AI-enabled product ideas, considering practical aspects like cost, latency, data requirements, and accuracy.
• • Differentiate between genuine technological capability shifts and market hype surrounding AI.
• • Help the broader organization understand when AI genuinely accelerates learning and when its impact is marginal.
• • Selectively launch bold, high-signal initiatives, even when short-term adoption metrics are uncertain, to test future market directions and establish technical leadership.
• • Treat these launches as genuine product bets, not mere demonstrations, to gather meaningful market feedback and shape perception.
• • Be explicit about the strategic intent behind each launch: whether it's primarily for learning, creating future optionality, or external market signaling.
• • This role is ideal for a seasoned Product Manager with a strong entrepreneurial spirit and a proven ability to navigate ambiguity and drive innovation from concept to validated decision.