You are given a daily time series Y_t representing the count of user reports of policy-violating content over the last 365 days for a single market. There are missing days, weekly seasonality, occasional outlier spikes due to takedown sweeps, and a suspected structural break around 2025-06-10 when a new detection rule launched. Tasks: 1) Characterize the series quickly: describe exactly how you would impute missing days, robustly estimate trend and weekly seasonality (e.g., STL with robust weights), and identify outliers that should be down-weighted rather than removed. 2) Test for a structural break near 2025-06-10: specify the exact change-point method (e.g., PELT with a piecewise-constant mean cost, or Bayesian Online Change Point Detection), your penalty/priors, and the decision rule for accepting a break (include thresholds you would tune and why). 3) Quantify the effect size: estimate the level shift (absolute and percent) attributable to the break after removing seasonality; report a 95% interval and explain the uncertainty source. 4) Forecast the next 14 days with 80% prediction intervals using a model appropriate for count data (e.g., Poisson or Negative Binomial with log link + seasonal dummies/prophet-like components). Explain how you would check calibration of intervals. 5) Causality follow-up: propose a lightweight validation design to attribute the change to the rule launch (e.g., geographic or traffic-channel holdout, phased rollout, or synthetic control). Specify the unit of analysis, pre-period length, primary metric, and the exact statistical test you would run. Include how you would guard against interference and seasonality biases. 6) Communicate results: provide the two most decision-relevant plots/tables you would include and the single-sentence takeaway you would give an exec if the break is real but beneficial.

This question evaluates competency in time series analysis, change-point detection, count-based forecasting, causal inference and experiment design, along with skills in handling missing observations, multiplicative weekly seasonality, outliers, structural breaks, effect sizing, and concise executive communication.

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This is a hard difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Google.

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This question is commonly asked for Data Scientist candidates at Google during technical interviews.

Analyze time series and design validation experiment

Daily Policy-Violation Reports: Robust Decomposition, Break Detection, Effect Sizing, Forecasting, and Causality

You are given a daily time series Y_t (counts of user reports of policy-violating content) over the last 365 days for a single market. The series has:

Missing days
Weekly seasonality (7-day)
Occasional outlier spikes tied to takedown sweeps
A suspected structural break near 2025-06-10 due to a new detection rule launch

Assume counts can be zero on some days and that weekly seasonality is multiplicative in nature.

Tasks

Characterize the series quickly: describe exactly how you would impute missing days, robustly estimate trend and weekly seasonality (e.g., STL with robust weights), and identify outliers that should be down-weighted rather than removed.
Test for a structural break near 2025-06-10: specify the exact change-point method (e.g., PELT with a piecewise-constant mean cost, or Bayesian Online Change Point Detection), your penalty/priors, and the decision rule for accepting a break (include thresholds you would tune and why).
Quantify the effect size: estimate the level shift (absolute and percent) attributable to the break after removing seasonality; report a 95% interval and explain the uncertainty source.
Forecast the next 14 days with 80% prediction intervals using a model appropriate for count data (e.g., Poisson or Negative Binomial with log link + seasonal dummies/prophet-like components). Explain how you would check calibration of intervals.
Causality follow-up: propose a lightweight validation design to attribute the change to the rule launch (e.g., geographic or traffic-channel holdout, phased rollout, or synthetic control). Specify the unit of analysis, pre-period length, primary metric, and the exact statistical test you would run. Include how you would guard against interference and seasonality biases.
Communicate results: provide the two most decision-relevant plots/tables you would include and the single-sentence takeaway you would give an exec if the break is real but beneficial.

Daily Policy-Violation Reports: Robust Decomposition, Break Detection, Effect Sizing, Forecasting, and Causality

You are given a daily time series Y_t (counts of user reports of policy-violating content) over the last 365 days for a single market. The series has:

Missing days
Weekly seasonality (7-day)
Occasional outlier spikes tied to takedown sweeps
A suspected structural break near 2025-06-10 due to a new detection rule launch

Assume counts can be zero on some days and that weekly seasonality is multiplicative in nature.

Tasks

Characterize the series quickly: describe exactly how you would impute missing days, robustly estimate trend and weekly seasonality (e.g., STL with robust weights), and identify outliers that should be down-weighted rather than removed.
Test for a structural break near 2025-06-10: specify the exact change-point method (e.g., PELT with a piecewise-constant mean cost, or Bayesian Online Change Point Detection), your penalty/priors, and the decision rule for accepting a break (include thresholds you would tune and why).
Quantify the effect size: estimate the level shift (absolute and percent) attributable to the break after removing seasonality; report a 95% interval and explain the uncertainty source.
Forecast the next 14 days with 80% prediction intervals using a model appropriate for count data (e.g., Poisson or Negative Binomial with log link + seasonal dummies/prophet-like components). Explain how you would check calibration of intervals.
Causality follow-up: propose a lightweight validation design to attribute the change to the rule launch (e.g., geographic or traffic-channel holdout, phased rollout, or synthetic control). Specify the unit of analysis, pre-period length, primary metric, and the exact statistical test you would run. Include how you would guard against interference and seasonality biases.
Communicate results: provide the two most decision-relevant plots/tables you would include and the single-sentence takeaway you would give an exec if the break is real but beneficial.

Analyze time series and design validation experiment

Quick Overview

Daily Policy-Violation Reports: Robust Decomposition, Break Detection, Effect Sizing, Forecasting, and Causality

Tasks

Solution

Comments (0)

Analyze time series and design validation experiment

Quick Overview

Daily Policy-Violation Reports: Robust Decomposition, Break Detection, Effect Sizing, Forecasting, and Causality

Tasks

Solution

Comments (0)