Instacart·Data Scientist·Technical Phone Screen·Senior

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Jul 2026

Summary

Technical phone screen for a Data Scientist role at Instacart. One meaty coding problem the whole time, no small talk, just straight into the implementation.

Questions Asked (1)

Implement a pivot table operation from scratch: given a list of row dicts and a pivot spec (index columns, column columns, values column, and an aggregation function), return the pivoted result as a nested dict or 2D structure.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

This felt more like a data engineering question than a data science one, which threw me a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the pivot spec and edge cases (e.g., missing values, multiple values per cell, aggregation function behavior). Then outline a two-pass algorithm: first group rows by index and column keys, then aggregate values within each group and assemble the nested dict or 2D structure. Discuss trade-offs between nested dict and 2D array representations, and mention how to handle missing combinations.

Pro tip: Demonstrate awareness of real-world data issues like duplicate entries, missing values, and non-numeric aggregations; also mention that in production you'd likely use pandas.pivot_table, but implementing from scratch shows deeper understanding.

1. Clarify requirements and edge cases

Ask about the expected output format (nested dict vs 2D array), how to handle missing combinations, and whether the aggregation function can be arbitrary (e.g., sum, mean, count).

2. Design the data structures

Choose a nested dict for flexibility or a 2D array for efficiency. Plan to use a dictionary to map (index_key, column_key) to a list of values for aggregation.

3. Implement the grouping and aggregation

Iterate over rows, extract index and column keys, and accumulate values in a temporary structure. Then apply the aggregation function to each group.

4. Assemble the final pivot structure

Build the nested dict or 2D array from the aggregated results, ensuring all index and column keys are represented, filling missing values appropriately.

5. Analyze complexity and trade-offs

Discuss time and space complexity (O(n) for grouping, plus aggregation cost) and trade-offs between nested dict and 2D array, including memory and lookup speed.

Key Points to Mention

Handling of duplicate (index, column) pairs by aggregating multiple values
Edge cases: empty input, missing values, non-numeric aggregations
Choice of output structure: nested dict (flexible, sparse) vs 2D array (dense, efficient)
Time and space complexity analysis
Comparison with pandas.pivot_table and when to use built-in vs custom implementation
Importance of clarifying the aggregation function's expected input (list of values vs single value)

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