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Director of Product, Lab at Fundraise Up. The Lab is Fundraise Up's 0→1 track. This is a player-coach role. You'll build and lead the Lab function while personally owning 1–2 of the highest-leverage bets. Your job is to reduce uncertainty, not ship features. Success = learning velocity, decision quality, and team leverage — not output volume or adoption.
Job Responsibility:
Portfolio & Strategy: Own the Lab bet portfolio. Size opportunities, set entry/exit criteria, and make decisive scale/iterate/kill calls. Maintain a visible exploration backlog.
Experimentation: Frame experiments around the single riskiest assumption. Define kill criteria before building. Choose the right fidelity (prototype, spike, wizard-of-oz, no-code, live pilot). Ship fast and protect learning speed.
Investment Decisions: Synthesize results into opinionated recommendations. Communicate what was tested, learned, and what remains unknown. Kill zombie initiatives — every experiment ends with a decision.
Handoffs: When a bet shows strong signal, prepare validated hypotheses, evidence packs, risks, and a proposed scaling model. Transfer ownership fully.
GTM & Partnerships: Source and close lighthouse pilots with clear scope, metrics, and off-ramps. Identify enabling partners and join strategic calls to move pilots to business validation.
Team: Build a repeatable Lab Operating System (pipeline reviews, kill reviews, demo days, post-mortems).
AI & Transparency: Use AI tools to accelerate research, synthesis, and prototyping. Explore AI-enabled product ideas with a realistic lens on cost, data, and accuracy. Publish decision memos and share failed experiments openly.
Requirements:
8–10+ years in Product with a proven 0→1 track record
Personally owned multiple high-risk bets with explicit go/kill decisions
3+ years managing PMs (player-coach or group lead)
Fluency across validation tools: discovery, prototypes, engineered MVPs — knows when each is appropriate
Cross-functional leadership across Eng/Design/Analytics and GTM
comfort in pre-sales/pilot settings
Experience where learning speed mattered more than polish and real downside risk was present
Hands-on experience using AI as a product-building and exploration tool
Can prototype with LLMs, APIs, or modern tooling and ship a functional prototype quickly
Able to scope AI experiments realistically and judge feasibility without full engineering validation
Can spot capability shifts and turn them into testable hypotheses
Evaluates build vs. buy vs. partner decisions
Nice to have:
Ex-founder with a clear ship/scale/kill story and measurable outcomes
Payments, fintech, e-commerce, or nonprofit domain experience
EU market familiarity (PSD2/SCA, data/privacy norms) and additional languages