Product Manager, Lab

📋 Description

• • As a Product Manager for the Lab at Fundraise Up, you will be at the forefront of innovation, tasked with exploring, validating, and de-risking bold, high-uncertainty product opportunities that lie beyond the immediate scope of our core platform. This is a unique opportunity to shape the future of digital fundraising by identifying and nurturing nascent ideas, many of which may not appear to be direct extensions of Fundraise Up's current offerings.
• • Your primary objective is not to ship features, but to accelerate learning and reduce uncertainty. You will own promising ideas from their inception – from initial exploration and hypothesis framing, through rapid experimentation and pilot programs, to making critical investment decisions: to scale, pivot, or kill the initiative. The expectation is that most ideas will be terminated early, while a select few, backed by strong evidence, will graduate to our New Markets or Core product teams.
• • Success in this role is measured by the speed of learning and the quality of decisions made, rather than by the volume of output or adoption metrics. You will be instrumental in identifying emerging opportunities driven by new technologies, such as AI, advancements in data science, evolving platform capabilities, or significant infrastructure shifts. Your ability to translate weak signals and nascent technical possibilities into clear, testable product hypotheses will be crucial.
• • You will be expected to explore ideas even in the absence of a clearly defined buyer, market category, or demand signal. Maintaining an organized exploration backlog, complete with identified risks, underlying assumptions, and specific learning goals, will be a key responsibility.
• • A significant part of your role will involve designing experiments and defining clear kill criteria before any development begins. This means framing experiments around the single riskiest assumption and establishing explicit criteria for termination prior to building. You will need to select the appropriate fidelity for each experiment, ranging from low-fidelity prototypes and technical spikes to wizard-of-oz setups or live pilots, always building just enough to learn and avoiding premature optimization.
• • You will execute scrappy prototypes, Minimum Viable Products (MVPs), and pilots with minimal scope, working closely with Engineering, Design, Data, and Go-To-Market teams. Ruthlessly protecting learning speed and avoiding premature optimization will be paramount throughout the experimentation process.
• • Making clear, data-driven investment decisions is central to this role. You will synthesize experimental results into opinionated recommendations: Scale, Iterate, or Kill. This involves clearly communicating what was tested, what was learned, and what remains unknown. The goal is to avoid "zombie initiatives" by ensuring every experiment concludes with a definitive decision, and you must be prepared to terminate your own ideas quickly if the evidence is weak.
• • When an opportunity demonstrates strong potential, you will prepare it for a clean handoff to other teams. This includes providing validated value and problem hypotheses, evidence from experiments or pilots, clearly defined risks, assumptions, and success metrics, and a proposed ownership and scaling model. The Lab does not run scaled products, so a complete transfer of ownership is expected.
• • Operating transparently and sharing learnings across the organization is vital. You will maintain a visible Lab portfolio showcasing ongoing explorations, their rationale, and current signals. Publishing decision memos and learning summaries, and openly sharing learnings from failed experiments, will foster a culture of continuous improvement.
• • You will leverage AI to accelerate learning, using modern AI tools to speed up research, synthesis, prototyping, and experimentation. You will explore AI-enabled product ideas with a realistic perspective, considering factors like cost, latency, data requirements, and accuracy, and distinguishing genuine capability shifts from mere hype. Your insights will help the company understand when AI meaningfully accelerates learning and when it does not.
• • Finally, you will launch high-risk, high-signal initiatives, selectively introducing bold ideas even when short-term adoption is uncertain. These launches will be treated as genuine product bets, not mere demonstrations, used to test future categories, shape market perception, and signal technical leadership. You will be explicit about the intent behind each launch: whether it's for learning, creating optionality, or external signaling.