Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Amazon data scientist interview, looked like a technical screen but the prompt was basically a 30-minute timed case study on exploratory data analysis and dashboard design. Dense question, lots of moving parts, felt like they were testing whether you'd panic or actually have a system.

Questions Asked (1)

You're given an undocumented CSV mixing user events and purchases. In 30 minutes, walk through how you'd understand the dataset and build executive-ready visualizations. Specifically: what are your first 10 data checks, what charts would you propose, how do you choose aggregation granularity for a launch week, how would clock-skew issues show up in your visuals, and how would you describe a one-slide dashboard to a stakeholder?

Product Analytics & MetricsData ModelingStakeholder Management

Author's notes

This was a lot to hold in your head at once.

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AI HintsAI Generated

Suggested Approach

Start by framing a structured 30-minute plan: first 10 minutes on data profiling and quality checks, next 10 on defining metrics and aggregation, last 10 on designing charts and the one-slide dashboard. Emphasize that you'd validate assumptions about event types, timestamps, and user identity before building any visuals, and that you'd choose granularity based on the launch week's traffic patterns and stakeholder needs.

Pro tip: Mention that you'd create a 'data dictionary' on the fly and share it with stakeholders to align on definitions, and that you'd use a time zone-aware timestamp check to catch clock skew early—this shows you think about data integrity and communication simultaneously.

1. Rapid Data Profiling

Run 10 quick checks: row count, column names/types, missing values, duplicates, date range, unique users, event type distribution, purchase amount stats, timestamp monotonicity, and time zone consistency. Document findings in a shared doc.

2. Define Metrics & Aggregation

Identify key metrics (e.g., daily active users, conversion rate, revenue) and choose aggregation granularity (hourly for launch day, daily for the week) based on traffic volume and stakeholder questions. Consider time zone normalization.

3. Design Visualizations

Propose charts: time series of events and purchases, funnel for conversion, cohort retention heatmap, and distribution of purchase values. Use clear labels and annotations for launch events.

4. Address Clock Skew

Detect clock skew by comparing timestamps across sources or checking for negative time differences. Visualize by plotting event counts over time; skew appears as spikes or gaps at boundaries. Mitigate by using server-side timestamps or adjusting to a common time zone.

5. Craft One-Slide Dashboard

Summarize key insights in one slide: headline metric, trend line, and one actionable takeaway. Describe to stakeholder as 'Here's what happened during launch week, why it matters, and what we should do next.'

Key Points to Mention

Data quality checks: missing values, duplicates, outliers, timestamp consistency
Metric definitions: DAU, conversion rate, revenue, and how they align with business goals
Aggregation granularity: hourly vs daily based on launch week traffic and stakeholder needs
Clock skew detection: time zone differences, negative time deltas, and visual anomalies
Chart selection: time series, funnel, cohort analysis, and distribution plots
Stakeholder communication: one-slide dashboard with clear narrative and actionable insights

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.