Practice real data science interviews tuned by the real interviewers who actually run them.

Facebook's News Feed team shipped a ranking algorithm change last week. Since then, comments per user are down 12% but time spent is up 5%. The VP wants to know: is this a problem?

Facebook is considering removing visible like counts from posts. Instagram already tested hiding likes in several markets. You need to think through how to measure the full impact of this change across users, creators, and the platform ecosystem.

The Facebook Newsfeed team is trying to understand whether users see enough friend content in their feed. The balance between friend posts, public content, and recommended content is critical to Facebook's value proposition. You need to figure out how to measure and optimize this.

The Reels team has flagged an alarming trend: 'average reels watched per session' has dropped precipitously over the past week. The VP wants answers by end of day. You need to diagnose what's happening and whether it's actually a problem.

Amazon's return rate jumped 15% week-over-week. The VP of Customer Experience wants answers by end of day. Walk me through your investigation.

YouTube's total watch time dropped 8% week-over-week. The VP of Product wants a root cause analysis before the quarterly business review. Multiple platform, regional, and content-type dimensions may be driving the decline, and early data suggests the drop is concentrated on mobile.

Google Play's privacy perception score improved 8% after launching prominent privacy labels, but Play Store engagement (app installs, browse time, return visits) dropped 14% in the same period. The VP of Trust & Safety wants to understand whether the privacy initiative is hurting engagement or if this is coincidental.

Average searches per user declined 4% globally, but average searches per user increased in every individual country. The CEO asks why the global number is going down if every country is improving. You need to diagnose and explain this paradox clearly enough for an executive audience.

Google Maps DAU dropped 10% over the past 2 weeks. The VP of Geo wants an explanation before the exec review. The drop could stem from a recent iOS update, a product change, an external factor, or a data quality issue. You need to systematically rule out hypotheses.

Rider cancellation rate in San Francisco jumped from 8% to 14% over the last week. Your team flagged this. Walk me through your investigation.

TikTok's recommendation team optimized for watch-through rate instead of engagement rate. Watch time is up 5%, but creator follower growth is down 8%. The creator partnerships team is concerned. Investigate.

Coinbase's total trading volume dropped 30% month-over-month. The CEO wants to understand if this is a market problem or a product problem. Walk through your investigation.

Cold food complaints have spiked 34% over the past 3 weeks across 12 metro areas. The VP of Customer Experience wants a root cause analysis. Two recent operational changes — batched delivery and prep time inflation — may be interacting in unexpected ways.

Average delivery time increased by 4.2 minutes last month across the platform. No major product changes were shipped. The operations team is alarmed and wants to know what's driving the increase and whether it requires immediate action.

Consumer order cancellations are up 28% in specific geographic markets over the past two weeks. The pattern is concentrated in suburban areas during dinner rush hours. You need to diagnose the root cause and recommend immediate actions.

Spotify's app installs dropped 25% month-over-month across all platforms. The CMO flagged this in the weekly business review and wants a root cause analysis within 48 hours. Multiple factors could be at play — marketing spend changes, competitive moves, platform-level shifts, and seasonality. You need to structure the investigation, identify the most likely causes, and size their impact.

Spotify's Trust & Safety team has flagged a spike in anomalous account behavior — accounts with listening patterns consistent with credential sharing, bot-driven stream farming, or compromised accounts. These accounts inflate streaming metrics, distort royalty payouts, and degrade recommendation quality. You need to design signals for detecting different types of anomalous behavior, set appropriate thresholds, reason about false positive tolerance, and define success metrics for a fraud detection system.

Netflix's primary engagement metric has dropped 20% week-over-week. Leadership is alarmed and wants a root cause analysis within 48 hours. The drop could be a data issue, a seasonal effect, a product change, a content gap, or a genuine shift in user behavior. You need to structure a systematic investigation and determine what's real vs. artifactual.

The PM responsible for Facebook Events has a new idea to drive engagement. When your friends mark that they'll attend an event, you'll receive a notification. How would you measure the success of this notification?

Facebook is creating a notification that alerts users when their Marketplace listing is about to expire. Your PM wants to measure the quality and effectiveness of this notification before a broader rollout.

Facebook is launching a new product called Facebook Restaurants — a Marketplace-like experience for discovering, reviewing, and ordering from local restaurants. You need to define the target users, success metrics, and measurement plan.

Facebook is launching a group chat feature similar to Discord channels within Facebook Groups. The goal is to increase real-time engagement within communities. You need to define success and guardrail metrics for the launch.

Meta's leadership has been pushing teams to optimize for user wellbeing, not just engagement. You've been asked to develop a framework for measuring whether a feature actually makes users happier — a much harder question than whether it drives engagement.

Instagram's Reels product has been live for over a year. Leadership wants a comprehensive health check: is Reels actually succeeding, or is it just cannibalizing other Instagram surfaces? You need to define the metric framework for evaluating Reels as a product line.

Amazon is launching 10% exclusive discounts for Prime members on select products. The goal is to increase Prime conversion and retention. How would you measure success?

Google Docs has never had a formal metrics framework. The new PM is tasked with designing one from scratch. The challenge: DAU is growing 6% QoQ, but docs created per user is declining 8%. Collaboration features are underutilized, and it's unclear whether Docs is winning on quality or just inertia. Design the top 5 metrics and identify the North Star.

Uber One is a $9.99/month subscription bundling Mobility ride discounts and Delivery free delivery fees. How would you measure its success?

TikTok's recommendation team wants to show more content from emerging creators in the For You feed. Currently, the top 5% of creators generate 60% of all views. The proposal: allocate 20% of feed slots to creators with fewer than 10K followers. How would you measure success?

Coinbase launched the Earn feature — users watch short educational videos about crypto projects and earn small amounts of that cryptocurrency. The feature is live and the PM wants to decide whether to expand it. How would you measure success, and what does the data tell you?

Reddit leadership wants a single 'Subreddit Health Score' metric displayed on an internal dashboard. You're tasked with defining it. The score should work across r/science (2M members, strict moderation) and r/memes (15M members, loose moderation).

DoorDash's Sponsored Listings product claims merchants see a 2.3x order lift, but the Growth team suspects selection bias is inflating the number. You need to audit true ad incrementality and determine whether the $18M/month ad product is delivering real value.

DoorDash launched Store Search — cross-restaurant item search (e.g., 'pad thai' returns results from multiple restaurants). Engagement looks great: 38% adoption, 15% of orders from search. But the VP of Product challenges whether it's creating new demand or just redistributing existing orders.

You've been asked to define the core metrics framework for understanding marketplace demand at DoorDash. This goes beyond simple order counts — you need metrics that capture demand health, unmet demand, and early warning signals for demand-supply imbalances.

Leadership wants a single health scorecard that captures the entire DoorDash marketplace across consumers, merchants, and Dashers. You need to define the metric hierarchy, handle conflicting metrics across sides, and design something stakeholders can actually use.

DoorDash launched alcohol delivery as a new vertical. You need to define success metrics that account for the unique characteristics of alcohol delivery — different purchase patterns, regulatory requirements, and potential cannibalization of food orders.

Spotify's Discover Weekly playlist has been live for years, but leadership wants a rigorous success measurement framework. The playlist generates high play counts, but the team debates whether play-through rate or save-to-library rate is the better signal of genuine discovery. You need to define success metrics, segment by user type, and propose an experiment design for testing algorithm changes.

Spotify Wrapped is the company's most viral annual feature. Leadership wants a comprehensive measurement framework that covers engagement, virality, retention, and brand impact. The challenge is that Wrapped is a once-a-year event, which makes traditional A/B testing nearly impossible. You need to define success metrics, measure social sharing impact, address the December DAU surge that doesn't stick, and propose evaluation methods for a feature you can only 'launch' once per year.

Netflix's recommendation engine drives 80% of content played on the platform, but leadership wants to understand whether it's truly successful or just convenient. The team debates whether click-through rate, completion rate, or long-term retention lift is the right north star. You need to define a rigorous success framework, handle the tension between 'good enough' recommendations and genuinely great ones, and design an experiment to test a new algorithm.

You are the PM for the Netflix homepage. Leadership asks: 'Is the homepage successful?' The challenge is disentangling homepage performance from overall Netflix performance. The homepage has recently been redesigned with larger hero content and fewer visible rows, and there's a debate about whether faster time-to-play or deeper browsing is the better outcome.

Netflix is launching a new personalized row labeled 'Because you watched [title]' to improve content discovery. The team needs a single North Star metric for discovery quality AND explicit anti-gaming constraints to ensure the metric reflects genuine discovery rather than inflated engagement. The previous row iteration increased play starts but not watch-through — a red flag for metric inflation.

The Instagram Monetization team wants to double the amount of ads shown on Instagram — it's the quickest way to nearly double revenue overnight. How would you determine the optimal ad-load for Instagram?

Facebook Search has observed a 10% increase in clicks on Events. Before celebrating, you need to figure out whether this is a good sign, a bad sign, or something more nuanced. The PM wants your analysis.

YouTube's recommendation algorithm update increased comments by 22% but decreased average watch time per session by 11%. The algorithm is surfacing more short, controversial, and opinion-driven content that generates comments but pulls users away from long-form content. The VP of Product wants to know which metric matters more for long-term retention.

TikTok LIVE's gift revenue is up 20% month-over-month, but the number of unique gift senders is down 10%. The growth team celebrates the revenue; the trust & safety team is worried. You're asked to investigate.

Coinbase is considering a 20% fee reduction (0.5% to 0.4% per trade) to compete with lower-cost exchanges. Design the experiment and evaluate whether it's worth shipping.

Reddit is considering a new ranking algorithm that surfaces posts from smaller subreddits users haven't joined. Early tests show higher DAU but increased toxicity in small communities. The Growth team is excited; the Trust & Safety team is worried.

An experiment improves consumer conversion by 3% but hurts Dasher acceptance rate by 2%. You need to recommend whether to launch. This tests your ability to reason about two-sided marketplace trade-offs without defaulting to a binary decision.

DoorDash is redesigning the Dasher payout structure. The current system has complaints about unpredictability and perceived unfairness. You need to design a new system that improves Dasher satisfaction while maintaining platform economics.

DoorDash's grocery delivery vertical has grown rapidly, but the restaurant delivery team is concerned about cannibalization. You need to determine whether grocery is additive to the platform or stealing from restaurant orders.

DoorDash runs an ad marketplace where restaurants pay for sponsored placement in search results. You need to measure whether these ads actually drive incremental orders or just cannibalize organic traffic, while balancing the interests of merchants, consumers, and the platform.

Spotify's recommendation system drives 35% of all listening hours. The ML team has a new model that improves click-through rate by 8% but reduces the diversity of recommended artists by 22%. The team is debating whether to ship it. You need to evaluate tradeoffs beyond CTR, define what 'good recommendations' means for a music platform, and propose a framework for balancing exploitation (playing what users already like) with exploration (helping users discover new music).

Facebook Groups wants to test a new feature: AI-generated discussion prompts posted automatically in groups with declining activity. The goal is to re-engage dormant groups. How would you design this experiment?

You're a data scientist at a food delivery company. The operations team ran a test where they gave Dashers a guaranteed minimum per delivery in a specific market. Results are in — but they're mixed.

Facebook is integrating Reels into the main Newsfeed experience. This is a major surface change that could boost short-form video consumption but risks cannibalizing existing content types. You need to design the experiment and measurement plan.

The Messenger team wants to improve the video call experience. They have several ideas — better connection quality, AR filters, screen sharing, and background blur. You need to design an experiment to test improvements and define what success looks like.

Amazon's search team developed a new ranking algorithm that prioritizes seller quality (reviews, fulfillment speed, return rate) more heavily. Design the experiment and evaluate the results.

Uber is replacing discrete surge tiers (1.0x, 1.2x, 1.5x, 2.0x) with dynamic continuous surge (any multiplier from 1.0x to 3.0x, updated every minute). Design the experiment and evaluate the results.

Reddit is redesigning new-user onboarding. Current: shows popular subreddits. Proposed: personalized recommendations based on stated interests. Design the experiment and evaluate the results.

DoorDash is launching in Austin (sprawling, car-dependent) and Portland (compact, bike-friendly). The current dispatch algorithm optimizes solely for distance-to-restaurant and was trained on suburban markets. It systematically under-assigns bike Dashers in dense areas. Design an experiment to test city-optimized dispatch.

DoorDash launched Peak Alerts — push notifications urging offline Dashers to go online during high-demand periods. Headline metrics look great: 28% more Dashers during alerts, 62% fewer unfulfilled orders. But deeper analysis may reveal that Peak Alerts cannibalize off-peak supply instead of creating net new supply.

The subscription team wants to test a $2/month price increase for DashPass (from $9.99 to $11.99). You're the data scientist tasked with designing this experiment. Pricing experiments have unique challenges — long-term churn effects aren't visible in short tests, and existing vs. new subscribers react very differently.

The marketing team wants to test a 5% off delivery fee coupon to drive consumer orders. But you know that many orders would have happened anyway. How do you measure the true incremental impact when marketplace dynamics create spillover effects?

You're testing a new real-time batching algorithm that assigns 2 orders to a single Dasher trip. This affects supply, demand, and timing across the entire local marketplace. Standard A/B testing breaks down because of interference effects — you need a switchback design.

DoorDash sends push notifications to offline Dashers during high-demand periods, asking them to go online. The operations team wants to know if this is actually effective at increasing supply, or if Dashers would have come online anyway.

DoorDash is testing a new restaurant ranking algorithm that prioritizes delivery speed and proximity over ratings and popularity. Early data looks promising for delivery times, but there are concerns about fairness to merchants and consumer discovery.

The hypothesis is that late deliveries cause customer churn. But you can't randomly assign late deliveries — you need to find a clever identification strategy to prove or disprove the causal relationship and size the business impact.

Netflix historically offered a 30-day free trial to new users, but recently eliminated it in many markets. Leadership wants to understand: was the free trial actually effective, and was removing it the right call? You need to define how you'd measure trial effectiveness, detect trial abuse, compare trial lengths, and evaluate the removal decision retroactively.

Netflix is testing a new trailer autoplay behavior on title cards — when users hover for 2 seconds, a 30-second trailer starts playing. The test shows higher play starts but unchanged total watch time. The team is split on whether this is a real UX improvement that helps users discover content faster, or metric inflation that makes the dashboard look better without creating real value.

Instagram's leadership is debating where to invest their next engineering cycle: doubling down on Reels (short-form video) or revamping Stories (ephemeral content). You're asked to present a data-driven framework for making this decision.

Facebook is considering adding a 'restaurants you may like' recommender in the News Feed. This would be a new content type competing for feed real estate alongside posts, Reels, ads, and stories. You need to design the system and measurement plan.

WhatsApp is seeing a surge in spam messages across both individual and group chats. You've been asked to design a spam detection system. The catch: WhatsApp is end-to-end encrypted, which limits what data you can use.

Meta's integrity team needs to estimate the prevalence of misinformation in Facebook Stories. Unlike the News Feed, Stories are ephemeral (24-hour lifespan) and often contain images, video, and text overlays — making automated detection harder. You need to design the measurement approach.

Gmail has 1.8 billion accounts but active user growth has plateaued at 2% annually. The VP of Gmail wants to increase the active user base by 15% in 18 months. You need to define the right metrics, identify where the activation funnel breaks, and design a referral experiment to drive growth beyond organic sign-ups.

Wrong orders are one of DoorDash's biggest customer pain points and refund cost drivers. You've been asked to design a machine learning system that minimizes wrong orders — but you first need to clarify what 'minimize' means and where in the pipeline to intervene.

DoorDash is considering expanding into 5 international markets. You need to build a prioritization framework that balances demand potential, operational feasibility, competitive landscape, and unit economics — then design a pilot to validate before committing.

You need to estimate consumer lifetime value (LTV) from first 30 days of behavior. This model will inform acquisition spend, retention investment, and growth strategy. The challenge: early behavior is noisy, promotional effects inflate initial usage, and different customer segments have wildly different trajectories.

DoorDash's refund costs are growing faster than order volume. Customer support suspects a significant portion of refund claims are fraudulent — users claiming missing or wrong items that were actually delivered correctly. You need to design a detection system and quantify the financial impact.

Spotify has invested heavily in podcasts. Early data shows podcast listeners retain 25% longer than music-only users, but their music streaming hours declined 18%. Leadership wants to understand the true impact of podcasts on customer lifetime value. There's a debate about whether podcast engagement genuinely increases CLV or if podcast adopters were already higher-value users (selection bias). You need to design an approach to isolate the causal effect.

Spotify's 1-month free Premium trial converts at 46%, but 54% of trial users revert to the ad-supported tier. The Growth team wants to understand which behaviors during the trial predict conversion, what nudges could improve conversion, and whether testing different trial lengths (7-day, 14-day, 3-month) could improve outcomes. You need to build a framework for diagnosing non-conversion and designing interventions.

Netflix has invested $17B in original content and needs a rigorous framework for measuring the performance of Netflix Originals. The challenge is that Originals serve multiple purposes — driving subscriptions, reducing churn, building brand, and filling catalog gaps. A simple viewership metric misses most of the value. You need to build a framework that informs renewal vs. cancellation decisions.

One million Netflix users have been inactive for six months — they're paying subscribers who haven't watched anything. Product and marketing teams want to re-engage them, but finance questions whether it's worth the investment. You need to segment these users, design a reactivation strategy, measure its effectiveness, and determine when the cost of reactivation isn't worth it.