Applied Scientist Interview Questions

Showing 31–41 of 41 results

• AmazonMedium·Applied Scientist
  Mean and Variance Through Maximum Likelihood

  Derive the maximum likelihood estimators (MLE) for the mean and variance of a univariate Gaussian distribution, and analyze the bias of the resulting variance estimator.

  Statistics & MathOnsitemaximum-likelihoodgaussian-distributionbias-variance
• AmazonMedium·Applied Scientist
  Test whether two user populations differ

  This question evaluates competency in statistical inference and multivariate analysis, covering hypothesis formulation about equal distributions, preprocessing and normalization considerations, choice of statistical tests or modeling approaches, and interpretation of significance and effect size.

  Machine LearningOnsite
• MicrosoftMedium·Applied Scientist
  Fewest Distinct Values to Delete So at Least Half the Array Is Removed

  A coding question that asks for the fewest distinct values whose complete deletion removes at least half of an integer array. It tests reasoning about how often each value occurs, proving that a choice is minimal, and the exact meaning of half when the array length is odd.

  Coding & AlgorithmsOnsite
• MicrosoftMedium·Applied Scientist
  Insert Spaces to Rebuild a Sentence from Dictionary Words

  A coding question that asks you to insert spaces into a string that lost them so that every piece is a dictionary word, returning the smallest valid sentence or None when no split exists. It tests string segmentation with dictionary lookups, handling of impossible inputs, and a canonical tie-breaking rule.

  Coding & AlgorithmsOnsite
• MicrosoftMedium·Applied Scientist
  Maximum Subarray Sum of an Array Repeated K Times

  A coding question that first asks for the largest sum of a non-empty contiguous subarray, then for the same quantity when the array is written out k times back to back. It tests subarray reasoning across copy boundaries, all-negative inputs, and sums that overflow 32-bit integers.

  Coding & AlgorithmsOnsite
• MicrosoftMedium·Applied Scientist
  Predict Employee Attrition from an HR Dataset and Explain Its Drivers

  A machine learning design exercise: given a tab-separated HR dataset with features such as age, job role, monthly income and overtime, predict which employees will leave and explain which factors drive attrition. It covers feature processing, model choice, class imbalance, evaluation metrics, threshold selection and interpretability.

  ML System DesignOnsite
• NVIDIAMedium·Applied Scientist
  Research Project Deep Dive and Your AI-Agent Research Workflow

  Present a 20-minute deep dive into one research project covering motivation, research question, training pipeline, results and takeaways, then extend it to the internship problem. The round also asks how AI agents fit into your daily research workflow for writing, experiments and ideation, testing technical depth, honesty about results, and verification habits.

  Behavioral & LeadershipOnsite
• OpenAIMedium·Applied Scientist
  Extensible card-hand ranking: three of a kind, flush, pair and a discard rule

  A three-part object-oriented coding exercise built around a card game: rank hands by three of a kind, pair and high card, insert a flush category, then add a discard rule that removes hands that are not a pair, returning a log of each game. It tests extensible design, deterministic tie-breaking and absorbing rule changes cleanly.

  Software Engineering FundamentalsOnsite
• WayfairMedium·Applied Scientist
  Evaluate Post-Purchase Recommendation Emails

  Evaluate post-purchase recommendation emails through incremental profit, useful timing, customer eligibility, randomized holdouts, and unsubscribe or complaint guardrails.

  Product / Decision MakingOnsite
• WayfairMedium·Applied Scientist
  Rank Homepage Modules for an E-Commerce Product

  Rank e-commerce homepage modules using business objectives, seasonal eligibility, slot effects, slate diversity, cold-start exploration, and page-level experiments.

  ML System DesignOnsite
• WayfairMedium·Applied Scientist
  Simulate and Compare Multi-Armed Bandit Strategies

  Compare epsilon-greedy, UCB1, and Thompson sampling in a reproducible Bernoulli bandit simulation with reward, pseudo-regret, and repeated-run uncertainty.

  Machine LearningOnsite