I jumped straight to fraud and had to walk it back.
Start by acknowledging that while fraud is a valid hypothesis, there are several legitimate explanations for high-volume ordering during a flash sale. Structure your answer by categorizing hypotheses into user behavior, technical factors, and business logic, then suggest how to validate each with data.
Pro tip: Demonstrate a balanced perspective: don't dismiss fraud, but show that you consider the broader context of a flash sale, which naturally drives extreme behavior. Mention that you would look for patterns across the 80 orders (e.g., timing, product mix) to differentiate between hypotheses.
Ask clarifying questions about the account, such as its history, typical order volume, and whether the orders are for the same or different products. This helps narrow down plausible hypotheses.
Generate a list of legitimate reasons: bulk purchasing for resale, group buying, shared account among friends/family, automated purchasing tools (e.g., bots for personal use), or a promotion that incentivizes multiple orders.
Think about system glitches (e.g., duplicate order submissions), test accounts, or business rules that allow multiple orders per account. Also consider if the flash sale has limited-time offers that encourage rapid ordering.
Rank hypotheses by likelihood and impact, then outline how to validate each using data (e.g., check order timestamps, device fingerprints, payment methods, IP addresses, and product SKUs).
Suggest actions such as monitoring the account, reaching out to the user for verification, or adjusting fraud detection rules to reduce false positives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scenario (e.g., flash sale, product drop) and the data available, then propose a set of features that capture coordination signals (shared devices, IPs, timing) and compare them against organic buying patterns. Structure your answer around entity-level joins (user, device, IP, payment) and time-window aggregations to surface anomalies.
Pro tip: Emphasize that you would first check data freshness and latency—if the data pipeline is delayed, you might need to use streaming or near-real-time sources. Also, mention that you'd validate findings with a quick holdout or known fraud cases if available.
Ask about the event (e.g., product launch, ticket sale) and what data tables are accessible (e.g., user events, transactions, device fingerprints). Confirm the time window and whether real-time data is available.
List features that indicate coordination: shared device IDs, IP addresses, payment instruments, or shipping addresses across multiple accounts. Also consider synchronized timing of purchases (e.g., purchases within seconds).
Contrast with organic behavior: diverse devices/IPs, natural time gaps, varied payment methods, and normal user activity (e.g., browsing before buying). Organic high-volume often shows a power-law distribution of purchases per user.
Propose joining user accounts with device, IP, and payment tables. Aggregate by these entities to compute counts, distinct users, and time deltas. Use window functions to detect bursts.
Rank features by lift and feasibility. Suggest quick validation: compare distributions of coordinated vs. organic segments, or check against known fraud labels if available.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The cost matrix framing is what made this interesting.
Start by defining the decision problem and translating the cost matrix into expected cost minimization. Then propose a threshold-based strategy using model scores, and incorporate manual review as a third action by comparing expected costs. Finally, discuss how to optimize thresholds and evaluate trade-offs.
Pro tip: Emphasize that the optimal thresholds depend on the model's calibration and the base rate of fraud; suggest using cost-sensitive learning or threshold optimization on a validation set to minimize total expected cost.
Clarify that for each order, we can approve, reject, or send to manual review. Assign costs: false positive (approve fraudulent) = $120, false negative (reject legitimate) = $30, manual review = $1 per order plus potential costs if misclassified after review.
Assume we have a model that outputs a probability of fraud for each order. Discuss the importance of calibration and how to obtain well-calibrated probabilities.
For a given probability p, compute expected cost of approve: p * $120; reject: (1-p) * $30; manual review: $1 + p * (cost if manual review misses fraud) + (1-p) * (cost if manual review falsely flags). Simplify by assuming manual review perfectly classifies, so cost = $1.
Find probability thresholds where the optimal action changes. Approve if p < threshold1, manual review if threshold1 ≤ p < threshold2, reject if p ≥ threshold2. Solve for thresholds by equating expected costs.
Discuss how to evaluate the strategy using historical data or simulation, and monitor performance over time. Consider adjusting thresholds as costs or fraud patterns change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Had to make up numbers on the fly and be transparent about it.
Start by clearly stating your assumptions about traffic volume, conversion rates, and fraud rates. Then calculate GMV and loss for both scenarios: holding 50% of suspicious traffic for 30 minutes (which may allow some fraudulent transactions to complete) versus rejecting it outright (which may block legitimate transactions). Compare the net impact.
Pro tip: Emphasize that the decision depends on the trade-off between fraud loss and false positive loss, and that the optimal strategy often involves a cost-benefit analysis rather than a blanket rule.
State assumptions about daily traffic, suspicious traffic percentage, conversion rates, average order value, fraud rate, and false positive rate.
Compute the total GMV and loss from suspicious traffic under normal conditions (no intervention) to establish a baseline.
For holding 50% of suspicious traffic for 30 minutes, estimate the GMV from legitimate transactions that complete and the loss from fraudulent transactions that slip through.
For rejecting outright, estimate the GMV lost from false positives (legitimate transactions blocked) and the loss avoided from fraudulent transactions.
Compare the net GMV and loss between the two scenarios, and discuss which is better under the given assumptions and how sensitive the conclusion is to changes in assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as a trade-off between promotion effectiveness and abuse prevention, then propose a layered defense across coupon design, user limits, payment/address risk, and bot detection. Define guardrail metrics with thresholds that balance catching abuse and avoiding false positives, and tie each layer to measurable impact.
Pro tip: Emphasize that abuse prevention should not degrade legitimate user experience; propose A/B tests to measure false positive rates and iterate on thresholds. Also, mention that TikTok's scale requires automated, real-time systems with feedback loops.
Clarify what constitutes abuse (e.g., multiple accounts, bots, resellers) and set clear goals: reduce abuse by X% while maintaining conversion and user satisfaction.
Propose coupon types (e.g., single-use, time-bound) and per-user caps (e.g., one per user, per device, per payment method) to limit exploitation.
Set velocity limits on coupon claims per user/IP/device, and assign payment risk tiers based on transaction history, using stricter checks for high-risk tiers.
Compute address trust scores from delivery history and user behavior, and deploy bot detection (e.g., CAPTCHA, behavioral analysis) to block automated abuse.
Select metrics like abuse rate, false positive rate, coupon redemption rate, and set thresholds (e.g., abuse rate >5% triggers alert) with automated monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with geo-split over holdout because holdout feels weird during a flash sale where network effects matter less.
Start by clarifying the promotion's goal and the metrics that define success, then propose a randomized experiment (user-level A/B or geo-split) with a holdout group. Outline the monitoring plan with guardrail metrics, sequential testing, and pre-defined stop/rollback criteria to ensure statistical rigor and business safety.
Pro tip: Emphasize the importance of pre-registering the analysis plan and using sequential testing to allow early stopping without inflating false positives. Also, mention that for TikTok, network effects and content virality may require cluster randomization or geo-splits to avoid contamination.
Clarify the promotion's primary goal (e.g., increase engagement, conversions) and select primary, secondary, and guardrail metrics. Ensure metrics are aligned with TikTok's ecosystem (e.g., watch time, shares, ad revenue).
Choose between user-level A/B test or geo-split based on interference risk. Include a holdout group (e.g., 5-10% of users) to measure long-term effects. Specify randomization unit, sample size, and power analysis.
Set up real-time dashboards for key metrics, with anomaly detection. Use sequential testing or group sequential boundaries to allow interim analyses. Monitor guardrail metrics (e.g., user churn, app crashes) for harm.
Define success as a statistically significant improvement in primary metric without degradation in guardrails. Stop criteria: if primary metric shows no effect after reaching sample size, or if guardrails breach thresholds.
Specify explicit rollback triggers: e.g., if guardrail metrics drop by >2% with 95% confidence, or if primary metric shows significant negative impact. Include a communication plan for stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.