Data Analysis and Insight Generation Questions

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.

MediumTechnical

0 practiced

Implement a Python function that takes a transactions DataFrame with columns user_id, transaction_date (YYYY-MM-DD), amount and returns a weekly cohort retention table where rows are cohort_week (user's first-week) and columns are week offset (0..12). Provide efficient pandas code that considers memory/time for a dataset with ~10M rows and explain how you'd scale to 100M rows.

HardTechnical

0 practiced

You observed a 4% increase in retention after a new onboarding flow. Craft a concise executive one-paragraph narrative that includes: the metric with baseline and lift, statistical evidence and robustness checks summary, business impact estimate (e.g., ARR or retention lift translation), recommended next steps, and brief measurement plan for rollout. Then list 4 key dashboard bullets that support that narrative.

MediumTechnical

0 practiced

Explain an anomaly detection approach for a daily traffic metric that shows weekly seasonality and occasional one-off spikes. Describe preprocessing (deseasonalize with STL), choice of detector (e.g., ESD on residuals or Prophet residual outlier detection), threshold setting, and how you'd evaluate precision/recall for detected anomalies.

MediumTechnical

0 practiced

You're given five analytics experiments/features with estimated impact, confidence, and implementation effort. Describe a structured prioritization framework (e.g., ICE, RICE) you would use to pick which to implement first. Explain how you'd factor in dependencies, risk, learnings value, and technical constraints when ranking experiments.

EasyTechnical

0 practiced

You're tasked with defining the primary success metric for a product recommender system whose business goal is to increase long-term retention. Propose one primary metric tied to retention, two secondary metrics that capture short-term engagement, and two guardrail metrics to ensure no negative side effects. Explain how each connects to retention and measurement considerations.

Unlock Full Question Bank

Get access to hundreds of Data Analysis and Insight Generation interview questions and detailed answers.

Join thousands of developers preparing for their dream job.