Amazon·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Amazon data scientist interview with a practical EDA scenario. The question was open-ended enough that I kept second-guessing whether I was going too deep into the technical weeds or not deep enough for the business angle.

Questions Asked (1)

You're handed an unfamiliar dataset and need to quickly surface insights for business stakeholders. Walk through how you'd profile the data, assess its quality, and decide which visualizations to build.

Product Analytics & MetricsAdaptability & AmbiguityStakeholder Management

Author's notes

I started with the profiling angle which felt right, row counts, column types, null rates, that kind of thing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business question and stakeholder needs, then walk through a structured data profiling and quality assessment process. Emphasize iterative visualization development, starting with simple overviews and refining based on feedback. Highlight how you prioritize insights that drive decisions.

Pro tip: Always tie your data profiling and visualization choices back to the specific business decision or metric the stakeholder cares about—this shows you're not just analyzing data but enabling action.

1. Clarify Objectives and Stakeholder Needs

Ask questions to understand what decisions the stakeholders need to make and what metrics matter most. This ensures your analysis is focused and relevant.

2. Profile the Data

Examine the dataset's structure, size, and types; compute summary statistics and distributions for each variable. Identify key dimensions, measures, and potential relationships.

3. Assess Data Quality

Check for missing values, duplicates, outliers, inconsistencies, and data type issues. Document any limitations and decide on cleaning or imputation strategies.

4. Develop Visualizations Iteratively

Start with simple overviews (e.g., histograms, bar charts) to understand distributions and trends. Then build more complex visuals (e.g., scatter plots, heatmaps) to explore relationships, refining based on stakeholder feedback.

5. Communicate Insights and Next Steps

Present key findings with clear, actionable visualizations, highlighting data quality caveats. Suggest further analyses or data collection to address gaps.

Key Points to Mention

Data profiling techniques: summary statistics, data types, distributions, and uniqueness checks.
Data quality dimensions: completeness, accuracy, consistency, timeliness, and validity.
Visualization best practices: choosing the right chart type for the data and message, avoiding clutter, and using color effectively.
Stakeholder communication: tailoring insights to the audience, using plain language, and focusing on actionable takeaways.
Iterative approach: starting simple and refining based on feedback to ensure relevance.
Amazon leadership principles: customer obsession, bias for action, and dive deep.

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