← Thumbtack Interview Insights

Thumbtack·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Thumbtack data scientist interview with a Monte Carlo simulation problem. Pretty much a coding screen dressed up as a stats question, which I didn't fully expect going in.

Questions Asked (1)

Q1

Simulate the Monty Hall problem using Monte Carlo methods to estimate the win probability for both the 'stay' and 'switch' strategies, given n rounds per strategy. Use a fixed random seed and output both probabilities rounded to 2 decimal places.

Algorithms & Data StructuresA/B Testing & Experimentation
Author's notes

I knew the theoretical answer going in (1/3 vs 2/3) so my first instinct was to just print those values and call it done.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, clarify the problem setup and assumptions (e.g., 3 doors, one car, host always reveals a goat and offers a switch). Then, outline a Monte Carlo simulation with a fixed random seed, running n rounds for each strategy, and compute the empirical win probabilities. Finally, round the results to two decimal places and discuss the theoretical probabilities (1/3 for stay, 2/3 for switch) as validation.

Pro tip: Emphasize the importance of a fixed random seed for reproducibility and mention that with large n, the Monte Carlo estimates should converge to the theoretical probabilities, demonstrating the law of large numbers.

1. Clarify assumptions and parameters

Confirm the rules: 3 doors, one car, host knows where the car is, always opens a goat door, and always offers the switch. Define n as the number of rounds per strategy and set a fixed random seed.

2. Design the simulation logic

For each round, randomly place the car, have the contestant pick a door, and have the host reveal a goat door. Then record the outcome for staying with the original pick and for switching to the other unopened door.

3. Implement and run the simulation

Write code (e.g., Python) to run n rounds for each strategy, using the fixed seed. Count the number of wins for stay and switch, then divide by n to get probabilities.

4. Round and present results

Round the win probabilities to two decimal places and present them clearly. Optionally, compare with theoretical values (1/3 and 2/3) to validate the simulation.

5. Discuss implications and extensions

Explain why switching is advantageous and how this relates to A/B testing (e.g., comparing strategies). Mention that increasing n improves accuracy and discuss potential variations (e.g., more doors).

Key Points to Mention

  • The Monty Hall problem's theoretical probabilities: 1/3 for staying, 2/3 for switching.
  • The importance of a fixed random seed for reproducibility in Monte Carlo simulations.
  • The law of large numbers: as n increases, the estimated probabilities converge to the theoretical values.
  • The simulation must correctly model the host's behavior: always reveals a goat and always offers the switch.
  • The connection to A/B testing: comparing two strategies (stay vs. switch) using simulated data.
  • Potential pitfalls: not accounting for the host's knowledge, or incorrectly modeling the switch (e.g., randomly choosing among remaining doors).

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