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Pinterest·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Quick Python screen for a Data Scientist role at Pinterest. One coding question, felt more like a warmup than a real technical deep-dive, but they clearly wanted to see if you knew your way around algorithmic thinking in Python.

Questions Asked (1)

Q1

Write a Python function that returns the first n prime numbers.

Algorithms & Data Structures
Author's notes

Went with trial division first just to show I understood the basics, then mentioned a sieve as the cleaner option.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., input validation, return type) and then present a clean, efficient solution using a sieve-based approach for generating primes up to a limit, or trial division with optimizations. Explain the algorithm's time and space complexity and discuss potential improvements or edge cases.

Pro tip: Mention that for large n, a segmented sieve or incremental sieve can be more memory-efficient, and always test with small n (e.g., n=0,1,2) to catch off-by-one errors.

1. Clarify requirements

Ask about input constraints (e.g., n can be 0 or negative), expected return type (list or generator), and performance requirements. Confirm whether the function should handle large n efficiently.

2. Choose an algorithm

Decide between trial division (simple but slower for large n) and sieve of Eratosthenes (faster but requires an upper bound). For unknown upper bound, use an incremental sieve or estimate the nth prime.

3. Implement the solution

Write clean, readable code with meaningful variable names. For sieve, estimate an upper bound using the prime number theorem (n log n + n log log n for n >= 6) and generate primes up to that bound, then slice the first n.

4. Analyze complexity and edge cases

Discuss time and space complexity (e.g., O(n log log n) for sieve, O(n sqrt(n)) for trial division). Handle edge cases: n <= 0 returns empty list, n=1 returns [2], etc.

5. Test and optimize

Walk through test cases (small n, large n) and mention potential optimizations like using a bit array, skipping even numbers, or using a generator for memory efficiency.

Key Points to Mention

  • Sieve of Eratosthenes and its time complexity O(n log log n)
  • Trial division with optimizations (checking up to sqrt, skipping evens)
  • Prime number theorem for estimating the nth prime upper bound
  • Edge cases: n=0, n=1, negative n
  • Space-time trade-offs and memory efficiency (e.g., segmented sieve)
  • Python-specific optimizations (e.g., using bytearray, list comprehensions)

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