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Roblox·Data Scientist·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Roblox data scientist interview with a stats-heavy coding question around causal inference. The problem was well-defined but had enough edge cases to trip you up if you weren't thinking carefully about the data.

Questions Asked (2)

Q1

Given three parallel arrays representing period (pre/post), treatment group, and outcome values, implement a difference-in-differences estimator that computes the treatment effect across the four group-period cells.

A/B Testing & ExperimentationProduct Analytics & MetricsAlgorithms & Data Structures
Author's notes

The formula itself isn't hard once you see it written out.

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AI HintsAI Generated

Suggested Approach

First, clarify the data structure and the DiD assumptions, then compute the four cell means (treatment pre/post, control pre/post) and combine them into the DiD estimate. Write clean, vectorized code (e.g., using pandas groupby or numpy boolean masks) and validate with a simple example or sanity check.

Pro tip: Mention that you would check the parallel trends assumption and consider clustering standard errors at the user level, since Roblox experiments often involve repeated observations per user.

1. Clarify data and assumptions

Confirm that the arrays are aligned and that the treatment and control groups are comparable. State the parallel trends assumption and note that DiD estimates the causal effect under this assumption.

2. Compute cell means

Calculate the average outcome for each of the four group-period cells: treatment pre, treatment post, control pre, control post. Use vectorized operations for efficiency.

3. Calculate DiD estimate

Compute the difference in differences: (treatment post - treatment pre) - (control post - control pre). This is the estimated treatment effect.

4. Validate and interpret

Sanity-check the result with a small example or by verifying that the estimate matches a manual calculation. Discuss statistical significance and potential confounders.

Key Points to Mention

  • Parallel trends assumption and its importance for causal inference
  • Vectorized implementation using pandas or numpy for efficiency
  • Handling missing or unbalanced data (e.g., unequal group sizes)
  • Clustering standard errors at the user level to account for repeated measures
  • Interpretation of the DiD coefficient as the average treatment effect on the treated
  • Potential violations of assumptions and sensitivity checks

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Extend the DID implementation to flag potential pre-trend violations: compute the pre-period difference between treated and control groups and return a boolean indicating whether it exceeds a given threshold.

A/B Testing & ExperimentationRoot Cause Analysis
Author's notes

This part I actually liked.

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AI HintsAI Generated

Suggested Approach

First, clarify the DID setup and the pre-period definition, then compute the difference in pre-period trends between treated and control groups. Compare this difference to the given threshold and return a boolean indicating whether it exceeds the threshold, while discussing the implications for the parallel trends assumption.

Pro tip: Emphasize that this is a heuristic check, not a formal test, and mention that the threshold should be chosen based on domain knowledge or sensitivity analysis to avoid false positives.

1. Clarify assumptions and inputs

Confirm the DID model specification, the definition of pre-period, and the threshold value. Ensure you understand the data structure (panel or repeated cross-section).

2. Compute pre-period group means

Calculate the average outcome for treated and control groups in the pre-period. If multiple pre-periods, compute the trend (e.g., slope) for each group.

3. Calculate the pre-period difference

Take the difference between the treated and control pre-period means (or trends). This represents the pre-existing gap or differential trend.

4. Compare to threshold and return boolean

Check if the absolute value of the difference exceeds the given threshold. Return True if it does, indicating a potential pre-trend violation; otherwise False.

5. Interpret and communicate

Explain the result in context: a True flag suggests the parallel trends assumption may be violated, warranting further investigation or robustness checks.

Key Points to Mention

  • Parallel trends assumption and its importance for DID validity
  • Definition of pre-period and how to handle multiple pre-periods (e.g., event study)
  • Choice of threshold: absolute vs relative, and sensitivity analysis
  • Statistical significance vs practical significance of pre-trend difference
  • Potential confounding factors and limitations of the heuristic
  • Implementation details: handling missing data, clustering, and weighting

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.