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Pinterest·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Pinterest data science interview that was heavier on coding fundamentals than I expected. The main problem was a combinatorics/enumeration thing and then they wanted a full test suite on top of it, which I wasn't totally prepared for.

Questions Asked (2)

Q1

Given several groups of hyperparameters where each group is a list of allowed values (e.g., learning rate, batch size, optimizer), write a function that returns all possible combinations across all groups, i.e., the Cartesian product.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I knew this was itertools.product territory immediately but fumbled explaining WHY that was the right abstraction.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present a clean recursive or iterative solution that builds combinations incrementally. Discuss trade-offs between approaches (e.g., recursion vs. itertools.product) and emphasize efficiency and scalability for large hyperparameter spaces.

Pro tip: Mention that in practice, the number of combinations can explode, so it's wise to consider lazy evaluation or generators to avoid memory issues, and to discuss how this applies to hyperparameter tuning at scale.

1. Clarify requirements and edge cases

Ask about input format, empty groups, and whether order matters. Confirm that the output should be a list of tuples or lists representing each combination.

2. Choose an approach

Decide between a recursive solution, iterative building, or using a library like itertools.product. Explain why you chose it, considering readability and performance.

3. Implement the solution

Write pseudocode or actual code, handling edge cases like empty input. For recursion, define base case and recursive step; for iterative, use a loop to expand combinations.

4. Analyze complexity and trade-offs

Discuss time and space complexity (O(N*M) where N is total combinations, M is number of groups). Mention memory implications and potential for lazy evaluation.

5. Relate to real-world application

Connect to hyperparameter tuning: how this function could be used to generate search space for grid search, and how to handle large spaces with sampling or early stopping.

Key Points to Mention

  • Cartesian product definition and mathematical foundation
  • Recursive vs. iterative implementation trade-offs
  • Use of Python's itertools.product for efficiency
  • Handling edge cases: empty groups, single group, no groups
  • Time and space complexity analysis
  • Scalability concerns and lazy evaluation (generators)

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

Q2

Design test cases for the hyperparameter combination function, covering: empty input, a single group with one value, a single group with many values, multiple groups, duplicate values within a group, and whether the output ordering is consistent across runs.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

This part tripped me up more than the actual coding.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the function's contract: inputs, outputs, and expected behavior for edge cases. Then systematically design test cases for each specified scenario, including both typical and boundary conditions. Finally, discuss how to verify output ordering consistency and handle duplicates.

Pro tip: Mention that you would use property-based testing (e.g., Hypothesis) to automatically generate combinations and verify invariants like output size and uniqueness, which demonstrates advanced testing maturity.

1. Clarify Requirements and Assumptions

Ask questions to understand the function's exact specification: input format (e.g., list of lists), output format (e.g., list of tuples), and expected behavior for edge cases like empty input.

2. Design Test Cases for Each Scenario

Create concrete test cases for empty input, single group with one value, single group with many values, multiple groups, and duplicate values within a group. Include expected outputs for each.

3. Address Output Ordering Consistency

Define what 'consistent ordering' means (e.g., lexicographic order of groups and values) and design tests to verify that the function produces the same order across multiple runs, possibly by sorting inputs or using deterministic algorithms.

4. Consider Additional Edge Cases and Properties

Think about other edge cases like large inputs, groups with empty lists, and performance. Also consider property-based tests to verify invariants such as the number of combinations equals the product of group sizes.

5. Summarize Testing Strategy

Conclude by explaining how you would implement these tests (e.g., using pytest), including parametrization for multiple cases and assertions for ordering consistency.

Key Points to Mention

  • Empty input should return an empty list or a single empty combination, depending on specification.
  • Single group with one value should return a list containing one tuple with that value.
  • Single group with many values should return each value as a separate combination.
  • Multiple groups should produce the Cartesian product, with size equal to the product of group sizes.
  • Duplicate values within a group should be preserved in the output, but if duplicates are not allowed, the function should deduplicate or raise an error.
  • Output ordering should be deterministic, e.g., by sorting groups and values, or by preserving input order consistently.

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