Katkat

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Gsa interview questions

3 practice questions in the style of Gsa's interviews, covering Machine Learning, Coding & Algorithms.

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Gsa interview questions by the numbers

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Katkat has 3 Gsa interview questions. The largest topics are Machine Learning (67%) and Coding & Algorithms (33%). By difficulty: 33% medium and 67% hard. The most common round is Technical Screen (100%). Frequent concepts include Machine Learning, Adversarial ML, Algorithms and Backtesting.

Topics

Difficulty

  • Medium33% · 1
  • Hard67% · 2

Interview rounds

  • Technical Screen100% · 3

Roles

  • Data Scientist100% · 3

Frequent concepts

  • Machine Learning67% · 2
  • Adversarial ML33% · 1
  • Algorithms33% · 1
  • Backtesting33% · 1
  • Computer Vision33% · 1
  • Data Ethics33% · 1

Showing 1–3 of 3 results

  • Critique a CNN-Based Bridge Safety ProposalMedium

    This question tests the ability to identify flaws in a computer vision project, focusing on data validity, label sparsity, adversarial robustness, and the practical limitations of predictive modeling in structural engineering.

    GsaData ScientistMachine LearningTechnical Screen
  • Optimize Matrix-Chain MultiplicationHard

    This problem tests your ability to solve a classic dynamic programming challenge, focusing on finding the optimal order of matrix multiplications to minimize the total number of scalar operations.

    GsaData ScientistCoding & AlgorithmsTechnical Screen
  • Predict Stock Returns from Sentiment and Market DataHard

    This problem tests the ability to design an end-to-end predictive pipeline for stock returns, focusing on time-series integrity, feature engineering, and robust evaluation metrics.

    GsaData ScientistMachine LearningTechnical Screen

Gsa interview experiences

All 1 →
  • A CNN Adversarial-Example Question on Bridge Collapse PredictionGSA · Data Scientist · Technical Screen · May 2026

    We talked about research. Explain why this approach would fail: collect bridges and their design drawings from Wikipedia. Train a CNN on these drawings to predict whether a bridge will collapse (or whether it will collapse within the first X years after being built). Then, starting from random noise, use gradient descent to "construct" a drawing plan that makes the model predict a 99.9% probabili…

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