Applying Data Science Techniques to Business Problems Questions
Recognizing when A/B testing is appropriate vs observational analysis. Suggesting SQL queries or analysis approaches that would answer the business question. Understanding when you'd need advanced modeling vs simpler analysis. Connecting technical approaches to business decisions (e.g., 'This cohort analysis would tell us whether the decline is from existing users or new users').
HardTechnical
0 practiced
Describe how you would use a causal forest or other HTE technique to detect heterogeneous treatment effects in an A/B test. Cover data preparation, model training, cross-fitting or honesty, validation, and how you'd present top actionable subgroups and uncertainty to product stakeholders.
HardTechnical
0 practiced
Your analytics team runs thousands of tests and metrics across segments. Explain multiple testing problems and propose operational strategies to control false discoveries, such as Bonferroni, Benjamini-Hochberg (BH), and hierarchical testing. Describe how you'd implement these corrections in an automated analysis pipeline.
MediumSystem Design
0 practiced
Design an A/B test to evaluate a personalized recommendations feature. Requirements: start at 5% of traffic, ramp to 50% over two weeks, measure overall lift and per-segment effects (new vs returning), and allow safe early-stopping during ramp if negative impact on key guardrails occurs. Explain randomization method, telemetry, statistical tests, and early-stop criteria.
MediumTechnical
0 practiced
Given sales(region, date, revenue) with treatment applied to region 'A' on 2024-07-01 and region 'B' as control, write an SQL or pseudocode implementation of a Difference-in-Differences regression: create post and treat indicators and estimate the interaction term. Be explicit about aggregation and sample construction.
MediumTechnical
0 practiced
Given events(user_id, event_date, revenue) and treatment(user_id, assigned boolean, assigned_at), outline an SQL-based approach to estimate incremental revenue attributable to treatment using matching or regression adjustment. Include pre-period baseline construction, matching logic, and assumptions and limitations of your approach.
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