Coinbase·Data Scientist·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

Coinbase data scientist interview with a pretty brutal coding question that felt more like a competitive programming problem than anything I'd call typical DS work. One question, lots of edge cases, and I left unsure if I'd handled the tie-breaking logic correctly.

Questions Asked (1)

Implement a function that takes a list of integers, identifies which of four sequence models (arithmetic progression, alternating-difference AP, geometric progression, or Fibonacci-plus-constant) best fits the sequence with at most one corrupted term, and returns the model name, its parameters, the index of the corrupted term if any, and the next term. The solution must run in O(n) time and O(1) space, and follow a specific tie-breaking priority when multiple models fit.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I stared at this for a solid minute before writing anything.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the exact definitions of each sequence model and the tie-breaking priority, then propose a single-pass algorithm that maintains minimal state for each model and uses a corruption-tolerant fitting strategy. Emphasize O(n) time and O(1) space by avoiding storing the entire sequence and using constant-time updates per element.

Pro tip: When multiple models fit, explicitly state the tie-breaking order and justify it—this shows attention to detail and prevents ambiguity in edge cases. Also, mention that you would validate the solution with adversarial test cases like sequences with the corruption at the first or last position.

1. Clarify definitions and constraints

Ask the interviewer to confirm the exact formulas for each model (e.g., arithmetic: a_n = a_1 + (n-1)d; alternating-difference AP: differences alternate between d1 and d2; geometric: a_n = a_1 * r^(n-1); Fibonacci-plus-constant: a_n = a_{n-1} + a_{n-2} + c) and the tie-breaking priority order. Also confirm that 'at most one corrupted term' means one term can be arbitrary, and the rest must fit the model exactly.

2. Design a single-pass, constant-space algorithm

For each model, maintain a small set of parameters (e.g., first term, common difference/ratio, etc.) and a corruption flag. Process the sequence left to right, updating parameters and detecting anomalies; when an anomaly is found, tentatively mark it as corrupted and continue, ensuring the remaining terms fit the model. Use O(1) space by not storing the sequence.

3. Handle corruption and model fitting

For each model, define how to detect and handle a single corrupted term: e.g., for AP, if the difference changes, check if skipping the current term restores the expected difference. For Fibonacci-plus-constant, use the recurrence to predict the next term and compare. Keep track of the corrupted index and ensure only one corruption is allowed.

4. Apply tie-breaking and select best model

After processing, collect all models that fit with at most one corruption. If multiple fit, choose according to the given priority order (e.g., arithmetic > alternating-difference AP > geometric > Fibonacci-plus-constant). Return the model name, its parameters, the corrupted index (if any), and the next term computed from the model.

5. Analyze complexity and edge cases

Explain that the algorithm runs in O(n) time because each element is processed once per model (constant number of models), and O(1) space because only a fixed number of variables are used. Discuss edge cases: sequences of length 1 or 2, corruption at the first or last position, and sequences that fit multiple models.

Key Points to Mention

Definition of each sequence model and how to compute parameters incrementally.
Strategy for handling at most one corrupted term without backtracking.
Tie-breaking priority and its implementation.
O(n) time and O(1) space justification.
Edge cases: short sequences, corruption at boundaries, multiple model fits.
Testing approach: unit tests with known sequences and corrupted variants.

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