Amazon·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Amazon data scientist round, focused on pandas and time-series aggregation over smart meter data. Pretty applied, no theory fluff, just write the code and explain your logic.

Questions Asked (1)

Given a table of smart meter readings with columns for meter ID, date, kWh consumed, and city, use pandas to find which date had the highest total energy consumption across all meters.

Product Analytics & MetricsAlgorithms & Data Structures

Author's notes

I went straight for groupby on the timestamp column then summed kwh_consumed, which was fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data structure and assumptions, then outline a pandas solution using groupby to sum kWh per date and idxmax to find the date with the highest total. Walk through the code step-by-step, explaining each operation and how you would validate the result.

Pro tip: Mention that you would first check for missing values or outliers in kWh, as they can skew the total, and consider whether the data includes partial days or multiple readings per meter per day.

1. Clarify the data and requirements

Confirm the table schema, ensure 'date' is a datetime column, and ask if there are any constraints like time zones or duplicate readings. Verify that 'kWh consumed' is numeric and handle missing values if needed.

2. Group by date and sum consumption

Use df.groupby('date')['kWh consumed'].sum() to compute total energy per date. This aggregates across all meters and cities.

3. Find the date with maximum total

Apply idxmax() on the summed series to get the date with the highest total. Alternatively, use nlargest(1) if you need the value as well.

4. Validate and present the result

Check the result by sorting or filtering to ensure correctness. Mention potential edge cases like ties and how you would handle them (e.g., return all dates or the first).

Key Points to Mention

Data cleaning: handling missing values, converting date to datetime, ensuring numeric types
Groupby aggregation: using sum() to total kWh per date
Finding max: using idxmax() or nlargest() to identify the date
Performance considerations: groupby is efficient for large datasets; avoid iterating rows
Validation: cross-check with alternative methods or visualizations
Edge cases: ties, time zones, partial days, and data quality issues

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