Analytical Background Questions
The candidate's analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
EasyTechnical
0 practiced
Describe the end-to-end engineering steps to compute daily DAU from raw event logs: include ingestion, validation and schema checks, storage schema, aggregation strategy, handling of late-arriving events, backfill policy, and monitoring you would implement to ensure data freshness and correctness.
MediumTechnical
0 practiced
You must compute the column mean and standard deviation for a numeric column in a CSV file that is too large to fit in memory (~100GB). In Python (no Spark), describe or provide an implementation/outline that computes mean and std in a numerically stable one-pass algorithm and explain complexity considerations.
HardTechnical
0 practiced
You're analyzing dozens of metrics across many experiments. Describe statistical methods and engineering guardrails to control false discoveries and prevent p-hacking: Bonferroni correction, Benjamini-Hochberg FDR, sequential testing approaches, pre-registration of metrics, and how to automate adjustments in analysis pipelines.
MediumTechnical
0 practiced
Explain the bias-variance tradeoff in supervised modeling. Provide practical examples where a product model exhibits high bias vs high variance, describe how you'd detect each using learning curves, and list remediation strategies for both scenarios.
HardTechnical
0 practiced
Design approaches for multi-touch attribution across marketing channels: compare simple heuristics (last-touch, first-touch), probabilistic Markov-chain models, and Shapley-value methods. Explain data requirements, interpretability, computational costs, and when a causal approach (uplift modeling or experiments) is preferable.
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