Plan and lead a large recommendation project

You are given a recommendation design problem, but the interviewer focuses on leadership and execution rather than detailed modeling.

Explain how you would:

1. Turn a high-level recommendation idea into an execution plan (milestones, dependencies, risks).
2. Estimate staffing: what roles you need (e.g., MLE, DS, backend, product, design, data engineering) and roughly how many people.
3. Propose a realistic timeline (phased rollout) and how you would coordinate cross-functionally.
4. Define success metrics and how you would run experiments.
5. Handle trade-offs, disagreements, and communication to stakeholders.

Solution

1) From idea to plan

• Clarify goal + scope: what user problem, where recommendations show up, what actions we optimize (click, start, completion, downstream retention).
• Write a 1–2 page spec:
  • Problem statement, users, surfaces.
  • Success metrics + guardrails.
  • Constraints (latency, privacy, fairness, explainability).
  • Assumptions and open questions.
• Decompose into workstreams:
  1. Data/Logging readiness
  2. Candidate generation
  3. Ranking model
  4. Serving + integration
  5. Experimentation + analytics
  6. Trust/safety/compliance
• Milestones (example):
  • M0: metrics + instrumentation finalized
  • M1: baseline heuristic recommender + offline eval
  • M2: first ML ranker + shadow launch
  • M3: A/B test + iteration
  • M4: scale + monitoring + handoff

2) Staffing estimate (typical)

Depends on surface criticality and infra maturity, but a common “medium-sized” build:

• Tech lead (you): 1
• MLEs: 2–3 (retrieval/ranking, training pipeline, evaluation)
• Data engineer: 1 (event pipelines, feature tables)
• Backend/infra engineer: 1–2 (online service, caching, latency)
• Data scientist/analyst: 1 (metrics, experiment analysis, guardrails)
• PM: 1 (requirements, stakeholder alignment)
• Design/UX: 0.5–1 (surface + explanations)
• Optional: Privacy/legal, Trust & Safety, SRE as part-time reviewers.

3) Timeline and phased rollout

A realistic phased approach (e.g., 12–16 weeks depending on infra):

• Weeks 1–3: requirements, logging gaps fixed, baseline established.
• Weeks 4–7: candidate generation + offline evaluation; first integration.
• Weeks 8–11: ranking model + calibration; shadow traffic; load testing.
• Weeks 12–14: A/B test, iterate, add re-ranking constraints (diversity/freshness).
• Weeks 15–16: ramp to 100%, monitoring, playbooks, post-launch review.

Cross-functional coordination:

• Weekly stakeholder sync; clear RACI; shared dashboard; written decision logs.

4) Success metrics and experimentation

• Primary: conversion aligned to product (e.g., starts/completions; qualified leads; retention).
• Guardrails: latency, bounce, hides/reports, content diversity, fairness segments.
• Experiment plan:
  • Pre-register hypotheses.
  • Define sample size/power.
  • Run staged ramp (1% → 10% → 50% → 100%) with stop conditions.

5) Trade-offs and conflict handling

• Make trade-offs explicit: “optimize CTR vs long-term value,” “freshness vs relevance,” “complexity vs reliability.”
• Use principles + data:
  • Start with simplest baseline that ships.
  • Use offline results to narrow options; use A/B tests to decide.
• Communication:
  • Frequent written updates.
  • Escalate early when timelines/risks change.
  • Document decisions and why alternatives were rejected.