Amazon·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Amazon ML engineer interview, got asked about data cleaning and prep. Pretty standard technical screen but the question had more depth to it than I expected going in.

Questions Asked (1)

How do you clean and prepare datasets that are messy or difficult to work with?

Technical Trade-offsRoot Cause Analysis

Author's notes

I rambled a bit here.

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AI HintsAI Generated

Suggested Approach

Start by emphasizing a systematic, iterative process: profile the data to identify issues, then apply targeted cleaning and validation steps, and finally document and automate the pipeline. Highlight trade-offs between data quality and time, and show how you prioritize based on business impact and model requirements.

Pro tip: At Amazon, data quality directly impacts customer experience, so always tie your cleaning decisions to business metrics and model performance. Mention that you version your datasets and cleaning code to ensure reproducibility and enable root cause analysis when issues arise.

1. Data Profiling and Assessment

Explore the dataset to understand its structure, missing values, outliers, duplicates, and inconsistencies. Use statistical summaries and visualizations to quantify the extent of messiness.

2. Define Cleaning Objectives and Priorities

Align with stakeholders to determine which data quality issues matter most for the model's goal. Prioritize based on impact on model performance and business metrics.

3. Apply Cleaning and Transformation Techniques

Handle missing values (imputation or removal), correct data types, standardize formats, treat outliers, and resolve duplicates. Use domain knowledge to guide decisions.

4. Validate and Iterate

Split data into train/validation sets and check for leakage. Evaluate model performance and iterate on cleaning steps if needed, ensuring no unintended bias is introduced.

5. Document and Automate

Record all cleaning steps, assumptions, and code in a reproducible pipeline. Automate the process for future datasets to ensure consistency and scalability.

Key Points to Mention

Handling missing data: strategies like mean/mode imputation, model-based imputation, or removal based on missingness mechanism.
Outlier detection and treatment: using statistical methods (z-score, IQR) or domain-specific thresholds, and deciding whether to cap, transform, or remove.
Data normalization and standardization: scaling features for algorithms sensitive to magnitude, and encoding categorical variables appropriately.
Data leakage prevention: ensuring cleaning steps are applied only to training data and not validation/test, to avoid optimistic performance estimates.
Trade-offs: balancing thoroughness with time constraints, and choosing simpler methods when they suffice for the model's needs.
Root cause analysis: investigating why data is messy (e.g., upstream pipeline issues) and addressing the source to prevent recurrence.

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