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Pinterest·Data Scientist·Technical Phone Screen·Senior

Senior
May 2026

Summary

Pinterest data science interview that leaned heavier on CS fundamentals than I expected. The main problem was a system design slash coding hybrid around sparse matrix storage, and they really wanted you to reason through the tradeoffs out loud.

Questions Asked (1)

Q1

Design a data structure to store two sparse matrices and implement print(), add(), and multiply() operations. Discuss the time and space complexity of each.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I knew dictionary-of-keys was a thing but blanked on the name mid-interview and ended up describing it by hand, which was fine but felt sloppy.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the sparse matrix representation (e.g., coordinate list, CSR, or dictionary of keys) and justify your choice based on the operations. Then, for each operation, describe the algorithm, derive time and space complexity, and discuss trade-offs. Finally, connect the design to Pinterest's data science context, such as handling large user-item interaction matrices.

Pro tip: Emphasize that the best representation depends on the sparsity pattern and operation frequency; for example, CSR is great for row-wise operations but less so for random updates. Mentioning real-world constraints like memory and parallelization shows maturity.

1. Clarify requirements and choose representation

Ask about matrix dimensions, sparsity level, and whether operations are frequent or one-time. Choose a sparse representation (e.g., dictionary of keys, list of lists, CSR) and justify it.

2. Design print() operation

Explain how to iterate over non-zero elements and output them, possibly in a readable format. Discuss time complexity O(nnz) and space O(1) extra.

3. Design add() operation

Describe merging two sparse structures, handling overlapping indices. Analyze time O(nnz1 + nnz2) and space O(nnz1 + nnz2) for the result.

4. Design multiply() operation

For matrix multiplication, discuss efficient sparse algorithms (e.g., using CSR and iterating over non-zeros). Analyze time complexity based on sparsity and dimensions, and space for the result.

5. Summarize trade-offs and optimizations

Compare representations, mention potential optimizations (e.g., blocking, parallelization), and relate to Pinterest's scale (e.g., billions of pins).

Key Points to Mention

  • Sparse matrix representations: coordinate list (COO), compressed sparse row (CSR), dictionary of keys (DOK), and their trade-offs.
  • Time complexity for add: O(nnz1 + nnz2) and for multiply: O(nnz1 * avg_nnz_per_row) or O(n^3) in worst case, but typically much less for sparse.
  • Space complexity: O(nnz) for storage, and O(nnz_result) for operations.
  • Handling of zero values and potential floating-point precision issues.
  • Use of efficient libraries (e.g., SciPy sparse) in practice, but ability to implement from scratch.
  • Relevance to Pinterest: user-item interaction matrices, graph adjacency matrices, and recommendation systems.

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