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.
HardSystem Design
0 practiced
Design a data model for storing event-driven telemetry optimized for both ad-hoc SQL analysis and high-performance aggregations for dashboards. Consider trade-offs for nested JSON properties vs. flattened columns, partitioning strategy, columnar storage choices, schema evolution, and retention. Provide a sample schema and rationale.
HardTechnical
0 practiced
Compare probabilistic forecasting models (that provide predictive distributions) versus deterministic rule-based forecasts for inventory planning. Discuss pros/cons, how to incorporate promotions and lead times into probabilistic forecasts, and how forecast uncertainty would change safety stock calculations.
HardSystem Design
0 practiced
Design a near-real-time analytics pipeline (<=5 minute latency) to power dashboards for a high-traffic e-commerce site. Include components for ingestion, stream processing, stateful aggregations, storage for ad-hoc queries, and pre-aggregation. Discuss trade-offs around cost, consistency (exactly-once), and complexity.
MediumTechnical
0 practiced
Explain precision, recall, and F1-score in classification tasks. Give one business example where precision is more important than recall, explain why, and describe how you would adjust classification thresholds to prioritize precision.
MediumTechnical
0 practiced
When would you use time-series decomposition (trend/seasonality/residual) versus a forecasting model like Prophet or ARIMA? Provide an example plan to forecast monthly revenue for the next 12 months including handling promotions, holidays, and irregular events.
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