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

70 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.

EasyTechnical

76 practiced

Given a transactional dataset where typical users make 1–3 small purchases but a few customers have extremely large amounts, describe three practical outlier detection approaches you would apply (statistical, transformation-based, model-based). Explain how you'd validate detected outliers before taking action.

MediumTechnical

80 practiced

When and why would you apply time-series decomposition into trend, seasonality, and residual components? Describe visual and statistical diagnostics to detect seasonality and stationarity (e.g., ACF, ADF), and how decomposition influences modeling choices like ARIMA, SARIMA, or Prophet.

HardTechnical

64 practiced

You observe a sudden conversion funnel drop that only appears on iOS. Outline an investigation plan covering: data validation (per-platform event counts), SDK/telemetry/version checks, recent app releases or feature flags, segmentation by OS/build/region, and quick mitigations (rollback, throttling) while you diagnose root cause.

MediumTechnical

65 practiced

You must detect anomalies across millions of metric time series (per user or per product). Propose a scalable architecture and approach that balances compute and accuracy: include aggregation frequency, lightweight detectors per-key, sampling/ensemble strategies, alert prioritization, and how to reduce false positives.

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