Spent the first few minutes trying to figure out whether they wanted an MLE answer or a PM answer.
Start by framing onboarding as a funnel with clear stages (sign-up, first visit, first action, retention) and identify friction points at each stage using data and user research. Then define success metrics tied to long-term retention and engagement, prioritize the biggest friction points by impact and effort, and design A/B tests to validate solutions. As an ML engineer, emphasize how ML can personalize and optimize the onboarding flow.
Pro tip: Tie every proposed change to a measurable metric and show how you'd use ML to personalize the experience at scale, but always validate with experiments before full rollout. Also, consider the cold-start problem for new users and how ML can mitigate it.
Break down the onboarding journey into stages (e.g., landing, sign-up, interest selection, first post/comment/vote) and use data (drop-off rates, time spent) and qualitative research to pinpoint friction points.
Choose metrics that reflect both immediate onboarding success (e.g., completion rate, time to first action) and long-term outcomes (e.g., D7/D30 retention, posts per user). Ensure they align with Reddit's goals.
Use a framework like RICE or impact/effort to rank friction points. Consider factors like user impact, business value, and technical feasibility, especially for ML solutions.
Propose A/B tests for each prioritized change, with clear hypotheses, control/treatment groups, and success criteria. For ML, suggest personalization (e.g., recommending communities) and measure its incremental impact.
Analyze experiment results, iterate on winning variants, and plan for scaling successful ML models while monitoring for long-term effects and potential biases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.