Ability to convert raw data into clear, evidence based business insights and prioritized recommendations. Candidates should demonstrate end to end analytical thinking including data cleaning and validation, exploratory analysis, summary statistics, distributions, aggregations, pivot tables, time series and trend analysis, segmentation and cohort analysis, anomaly detection, and interpretation of relationships between metrics. This topic covers hypothesis generation and validation, basic statistical testing, controlled experiments and split testing, sensitivity and robustness checks, and sense checking results against domain knowledge. It emphasizes connecting metrics to business outcomes, defining success criteria and measurement plans, synthesizing quantitative and qualitative evidence, and prioritizing recommendations based on impact feasibility risk and dependencies. Practical communication skills are assessed including charting dashboards crafting concise narratives and tailoring findings to non technical and technical stakeholders, along with documenting next steps experiments and how outcomes will be measured.
EasyTechnical
99 practiced
Given a new dataset for product engagement with columns: user_id, session_id, session_start (timestamp), session_duration_seconds, and events_count, describe an exploratory analysis plan. List the summary statistics and visualizations (histogram, boxplot, time series decomposition, pivot tables) you would compute, the objective for each (detect skew, seasonal patterns, anomalies, distribution tails), and three suspicious patterns you would flag for deeper investigation.
EasyTechnical
59 practiced
For a product metrics dashboard shown to product managers, list the best chart types for these tasks: 1) show trend over time for conversion rate, 2) show distribution of session length, 3) show funnel conversion from view->click->purchase, 4) compare metric by country segments, 5) show correlation between time-on-site and revenue. For each chart explain why it helps and one design tip to keep the message clear for non-technical stakeholders.
MediumTechnical
51 practiced
You run 30 A/B tests in parallel each reporting p-values for the same primary metric. Explain why multiple testing corrections are necessary, compare family-wise error rate control (Bonferroni) vs false discovery rate (Benjamini-Hochberg), and demonstrate how to apply Benjamini-Hochberg to a list of p-values in brief pseudocode or Python.
MediumTechnical
91 practiced
Product team reports a 12% week-over-week drop in click-through-rate (CTR) for a recommendation widget. Describe an end-to-end troubleshooting process: which datasets and joins you'd check, key sanity checks, segmentation analyses to perform, potential instrumentation bugs to look for, how to test if the drop is real vs sampling noise, and quick corrective actions to consider.
EasyTechnical
59 practiced
You receive a new events dataset from ETL. List at least eight automated data validation checks you would run before using it for analysis or model training. Include schema checks, distributional checks, sanity checks against previous days, referential integrity, uniqueness, and business-logic constraints. For each check, explain why it matters and what automated action or alert you'd configure if it fails.
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