I started talking about segmentation right away, which felt right, but I got a bit tangled trying to explain feature selection before I'd even defined the objective.
Start by clarifying the campaign objective (e.g., drive awareness, sign-ups, or viewership) and target audience, then outline a data-driven process: define success metrics, identify required data sources, build a model to predict engagement or conversion, and design an experiment to measure impact. Emphasize iterative testing and learning to optimize the campaign.
Pro tip: Show that you understand Amazon's customer-obsessed culture by focusing on the customer experience and long-term value, not just short-term clicks. Mention how you'd leverage Amazon's unique data assets (e.g., viewing history, purchase behavior) while being mindful of privacy and consent.
Clarify the campaign goal (e.g., drive new sign-ups, increase viewership of the series) and identify the target audience segments (e.g., existing Prime members, lapsed viewers, genre fans).
Determine what data is needed (e.g., customer demographics, viewing history, engagement metrics) and define success metrics (e.g., open rate, click-through rate, conversion rate, watch time).
Build a model to predict which customers are most likely to engage with the email and watch the series, using techniques like propensity modeling or collaborative filtering.
Design the email content and plan an A/B test to measure the impact of different subject lines, creatives, or send times, ensuring statistical power.
Analyze results, measure against success metrics, and iterate on the model and campaign strategy based on learnings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty much the core of the whole interview.
Start by clarifying the business goal and defining a clear success metric (e.g., click-through rate, conversion, or long-term engagement). Then propose a rigorous A/B test with proper randomization, sample size calculation, and guardrail metrics to measure the new ranking function's impact. Finally, discuss how to analyze results, check for novelty effects, and decide on rollout.
Pro tip: Emphasize the importance of guardrail metrics (e.g., latency, diversity, or user satisfaction) to ensure the new ranking function doesn't harm other aspects of the experience. Also, mention that you'd run a pre-experiment power analysis to avoid underpowered tests.
Identify primary metrics (e.g., CTR, conversion rate) and secondary/guardrail metrics (e.g., revenue, user retention, diversity). Ensure metrics align with business objectives and are measurable.
Set up a randomized controlled trial (A/B test) with proper randomization unit (e.g., user-level), determine sample size and duration via power analysis, and ensure control and treatment groups are comparable.
Launch the experiment, monitor for data quality issues, and check for sample ratio mismatch (SRM). Avoid peeking at results prematurely to prevent false positives.
Use statistical tests (e.g., t-test, bootstrap) to compare metrics between groups. Check for novelty effects, segment-level differences, and ensure practical significance.
Based on results, decide whether to roll out, iterate, or abandon the new ranking function. Consider long-term holdout groups to measure sustained impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer I gave: offline comparison first using a held-out set and rank-quality metrics, then narrow to top candidates for live testing, then pick based on business KPIs like watch time or retention.
Start by clarifying the business objective and the evaluation metric that matters most (e.g., revenue, engagement, or customer satisfaction). Then describe a systematic process: offline evaluation with appropriate metrics, online A/B testing, and consideration of trade-offs like complexity, latency, and maintainability. Emphasize that the 'best' function depends on the specific context and constraints.
Pro tip: At Amazon, always tie your choice back to customer impact and long-term value; mention that you would run a rigorous A/B test with a clear primary metric and guardrail metrics to avoid unintended consequences.
Clarify the business goal and select evaluation metrics that align with it (e.g., NDCG for ranking quality, CTR for engagement, or revenue per session). Ensure metrics are measurable and actionable.
Use historical data to compare ranking functions with appropriate offline metrics. Consider cross-validation and beware of biases like position bias in logged data.
Design and run a controlled experiment to measure the impact of each ranking function on key metrics. Ensure sufficient power and random assignment, and monitor guardrail metrics.
Evaluate non-performance factors such as computational cost, latency, scalability, maintainability, and interpretability. Consider the long-term implications of each choice.
Choose the function that best balances performance and trade-offs, then deploy and continue monitoring. Be prepared to revisit the decision as data and business needs evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.