Intuit·Data Scientist·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Interviewed for a Data Scientist role at Intuit and got a coding question that felt more like a software engineering screen than anything data-related. Just the one problem but they pushed pretty hard on the follow-ups.

Questions Asked (1)

Implement a function that returns the nth Fibonacci number. Be prepared to discuss time and space complexity and how you'd handle very large inputs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the naive recursive solution because my brain just went there first, and they immediately asked about stack overflow.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., expected input size, whether recursion is acceptable) and then present multiple solutions: naive recursion, memoization, iterative DP, and matrix exponentiation. Discuss the time and space complexity of each and recommend the iterative approach for general use, while mentioning matrix exponentiation for very large n. Finally, address handling very large inputs by using arbitrary-precision arithmetic or modular arithmetic if needed.

Pro tip: For very large n, mention that Fibonacci numbers grow exponentially, so you might need to return the result modulo a large prime or use a library like Python's built-in big integers. Also, note that the iterative approach uses O(1) space and O(n) time, which is optimal for a single query.

1. Clarify Requirements

Ask about constraints: what is the range of n? Should the function handle negative inputs? Is recursion allowed? Do we need exact values or modulo?

2. Present Multiple Solutions

Describe naive recursion (exponential time), memoization (O(n) time and space), iterative DP (O(n) time, O(1) space), and matrix exponentiation (O(log n) time).

3. Analyze Complexity

For each solution, state time and space complexity. Highlight that iterative is best for single queries, while matrix exponentiation is best for very large n or multiple queries.

4. Handle Large Inputs

Discuss that Fibonacci numbers grow exponentially, so for large n, exact values become huge. Suggest using arbitrary-precision arithmetic (e.g., Python's int) or modular arithmetic if only the remainder is needed.

5. Implement and Test

Write clean code for the chosen approach, handle edge cases (n=0, n=1), and test with small and large inputs. Mention potential optimizations like fast doubling.

Key Points to Mention

Time and space complexity of each approach: naive recursion O(2^n), memoization O(n) time and space, iterative O(n) time and O(1) space, matrix exponentiation O(log n) time and O(log n) space (or O(1) with fast doubling).
Trade-offs: iterative is simple and efficient for moderate n; matrix exponentiation is faster for very large n but more complex.
Handling very large inputs: use arbitrary-precision integers (e.g., Python's int) or modular arithmetic to avoid overflow and manage memory.
Edge cases: n=0 returns 0, n=1 returns 1, negative n may be undefined or require extension.
Optimization techniques: fast doubling method for O(log n) time and O(1) space.
Practical considerations: for data science roles, relate to dynamic programming concepts and efficient computation for large datasets.

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