SIG (Susquehanna)·Data Scientist·Technical Phone Screen
May 2026
SIG had me work through a pretty dense stats and experimentation problem set built around a delivery platform scenario. Five sub-questions covering sample size math, non-inferiority testing, multiple comparison corrections, cluster randomization, and sequential monitoring. No metadata on outcome or timing was shared.
- Given a baseline mean delivery time of 42 minutes with SD of 15 minutes and a target relative improvement of negative 5%, compute the per-arm sample size for a two-sample t-test using alpha 0.05 two-sided and 80% power.
- For a non-inferiority test on cancellation rates where the baseline is 6% and the acceptable margin is 0.5 percentage points, compute the per-arm sample size using one-sided alpha of 0.05 and 80% power.
- You have one primary metric, two guardrail metrics, and one secondary metric. Propose and justify an error control strategy across these metric families and explain how you'd report adjusted confidence intervals.
- Randomization is at the zone-day level with 300 orders per cluster and an ICC of 0.03. Compute the design effect and the adjusted per-arm sample size, then figure out how many zone-days per arm are needed.
- With four equally spaced interim looks using O'Brien-Fleming alpha spending, explain how early stopping boundaries compare to the final look boundary, and describe how this affects minimum detectable effect and trial runtime in practice.
“The mechanics here are standard: delta is 0.05 * 42 = 2.1 minutes, then plug into n = 2 * ((z_alpha/2 + z_beta) / delta * sigma)^2.” The rest of the author's notes on Data Scientist interview at SIG (Susquehanna), Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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