DoorDash Ambiguity & Curveball Product Framing

What's being tested

Interviewers are probing your ability to turn ambiguity into a clear, scoped product plan: ask the right clarifying questions, map assumptions to experiments, prioritize tradeoffs, and communicate a defensible recommendation. At DoorDash this verifies you can move fast in a complex two-sided marketplace with real operational constraints, balancing short-term customer experience with long-term unit economics. Expect to be judged on structure, measurable success criteria, and stakeholder-aware rollout plans.

Core knowledge

Clarifying questions you should always ask first: desired outcome, time horizon, success thresholds, target user segments, geographic constraints, and existing KPIs/benchmarks (baseline).
Assumption mapping: list top 3–5 hypotheses (e.g., supply shortage, routing inefficiency, inaccurate ETAs), rank by uncertainty × impact, and target experiments to reduce highest-expected-value uncertainty.
North-star vs guardrails: pick one north-star metric (e.g., GMV or on-time-%), plus 2–3 guardrails (cost-per-order, driver-earnings, refund-rate) to avoid harmful optimizations.
Metric framing: define metrics with numerator/denominator, segmentation (time-of-day, zip, store type), and whether they’re leading or lagging indicators; estimate expected variance and practical detectable deltas.
Prioritization formulas: know RICE = (Reach × Impact × Confidence) / Effort and ICE = Impact × Confidence × Ease; use Confidence to capture assumption risk explicitly.
Tradeoff taxonomy: categorize options as short-term operational fixes (manual incentives, refunds), medium-term experiments (A/B tests, routing parameter tweaks), and long-term product changes (UI redesign, marketplace policy).
Experiment strategy (PM-level): prefer small pilots → randomized experiments → ramped rollouts; define primary metric, risk guardrails, sample-size sanity check, and explicit rollback criteria.
Stakeholder map: identify decision owners across Operations, Engineering, Merchant Partnerships, Legal, and local marketplace leads; call out approvals and SLOs needed for rollout.
Rollout design: pilot in high-signal markets, monitor p90/p99 operational latencies, incremental rollout windows, and post-launch operational playbooks for exceptions.
Cost and unit economics: estimate incremental cost-per-order and impact on contribution margin; use expected-value calc: EV = Probability(success) × Impact − Cost.
Communication framing: structure answers as Problem → Hypotheses → Options (tradeoffs) → Recommendation → Metrics/Experiment Plan → Risks & Mitigations.
Regulatory / merchant ops flags: consider local labor rules, merchant SLA impacts, and required contract changes before large-scale incentives or fee changes.

Worked example

Sample prompt (typical): "How would you reduce on-time delivery failures by 10% in city X within 6 months?" First 30s: clarify the definition of on-time (customer ETA vs promised window), baseline rate, target granularity (all-orders vs specific segments), and whether budget exists for incentives. Organize your answer into three pillars: Diagnose (data + top failure modes), Fix (short-term operational levers, medium-term experiments, long-term product changes), and Rollout/Measure (pilot → A/B → ramp). For Diagnosis: propose quick funnel metrics (acceptance-rate, pickup-delay, travel-time variance) and a segmentation by merchant and time-of-day. For Fix: list concrete options — merchant SLA enforcement, driver incentives, ETA-model recalibration, customer communication. Explicit tradeoff: driver incentives can move metrics fast but increase cost-per-order and may not be sustainable; model fixes scale but take engineering time. Close by proposing a 6–8 week pilot in 1–2 high-impact zip codes, primary metric on-time-%, guardrails cost-per-order and driver-earnings, and say, "If I had more time I'd run parallel A/B tests on ETA adjustments and incentive levels, and build an automated rollback if guardrails breach."

A second angle

Different prompt: "Propose how DoorDash should enter a new city where merchant density is low." The same framing applies but constraints shift: the problem becomes supply-demand seeding rather than operational tweaks. Clarifying questions focus on market economics (expected order volume, competitor presence), time-to-profit expectations, and resource limits for merchant acquisition. Hypotheses might include: (1) customers will order if variety exists, (2) subsidized pricing will attract demand, (3) merchants need onboarding support. Solutions span marketplace seeding (merchant partnerships, promotions) and operational playbooks (dedicated drivers, scheduled batching). Prioritize by early signals: activation rate, CAC vs LTV proxies, and time-to-first-delivery. Tradeoffs: aggressive subsidies accelerate growth but distort long-term pricing; a phased approach with targeted promos and merchant incentives can validate demand before heavy subsidy spend.

Common pitfalls

Pitfall: Optimizing the wrong metric.
A common mistake is chasing an operational metric (e.g., reducing ETA variance) without proving it moves customer satisfaction or retention — always tie changes back to the north-star and economics.

Pitfall: Over-scoping or giving technical specs.
Interviewers want product judgment, not engineering design. Avoid describing low-level implementations; focus on user impact, prioritization, and measurement.

Pitfall: Ignoring stakeholder constraints.
Proposing broad system changes without acknowledging approvals, legal/regulatory limits, or merchant incentives looks unrealistic; call out required partners and success conditions.

Connections

Interviewers can easily pivot to adjacent areas like experimentation design (sample size, ramp strategy), marketplace economics (supply-side incentives, GMV), or operational playbooks (SLA enforcement, incident handling). Be ready to zoom into any of these while keeping product tradeoffs front-and-center.