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Short Description Role Area Topic Difficulty Seen in interview Solved Overall rating
Normality assumption Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Normality Medium Yes
Reducing variance in AB testing Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Variance Medium Yes
Sample Ratio Mismatch Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Sample Ratio Mismatch Medium Yes
Simpson’s paradox Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Simpson’s paradox Medium Yes
Questions worksheet (recruiter-screen) Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Application process Discussion prep, Application prep Medium Yes
Referrals vs. online applications Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Application process Application prep Easy No
Response time Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Application process Application prep Easy No
Timeline: from application to offer Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Application process Application prep Easy No
Questions worksheet (behavioral) Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Behavioral Discussion prep Hard Yes
Climbing stairs (Leetcode 70) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms Recursion, Dynamic programming Easy Yes
Find if Path Exists in Graph (Leetcode 1971) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms DFS, BFS Medium Yes
K Closest Points to Origin (Leetcode 973) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms Heap Easy Yes
Longest substring without repeating characters (Leetcode 3) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms Hash, Sliding Window Medium Yes
Minimum remove to make valid parentheses (Leetcode 1249) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms Stack Medium Yes
Number of recent calls (Leetcode 933) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms Queue, DEQueue Easy Yes
Remove duplicates in place (Leetcode 26) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms Array, Two pointers Easy Yes
Search in Binary Search Tree (Leetcode 700) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms Binary Search, Binary Search Tree Easy Yes
Sort an Array (Leetcode 912) Data Scientist, Applied Scientist, Machine Learning Engineer Data Structures and Algorithms Recursion, Sorting Medium Yes
Analyze prediction error Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Prediction error, Bias-variance tradeoff, Diagnostics, Learning curves Medium Yes
Bias-variance equation Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Bias-variance tradeoff, Formula derivation Easy Yes
Linear regression with gradient descent Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Coding Gradient descent, Linear regression Medium Yes
Sample from random generator Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Coding Sample, Uniform, Random number generator Medium Yes
Gradient descent vs. stochastic gradient descent Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Gradient descent, Stochastic gradient descent, Minibatch Medium Yes
Imbalanced dataset Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Class imbalance Medium Yes
k-means Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning k-means Medium Yes
L1 (Lasso) vs. L2 (Ridge) regularization Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning L1, L2, Redularization, Lasso, Ridge Medium Yes
Linear regression likelihood function Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Linear regression, Likelihood function, Formula derivation Medium Yes
Model interpretability Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Interpretability Easy Yes
Multiclass evaluation metrics Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Multiclass, Diagnostics Medium Yes
NDCG vs. Mean Average Precision (MAP) vs. Mean Reciprocal Rank (MRR) Applied Scientist, Machine Learning Engineer Machine Learning Ranking metrics, NDCG, Mean average precision (MAP), Mean Reciprocal Rank (MRR), Recommender Systems Medium Yes
ROC vs. PR curve Applied Scientist, Machine Learning Engineer Machine Learning AUC, ROC, AUPR, Precision, Recall, Evaluation metrics Medium Yes
Measuring counterfactual impact Product Data Scientist, Data Scientist Metrics Problem solving, Counterfactual Hard Yes
Sudden drop in user engagement Product Data Scientist, Data Scientist Metrics Problem solving, Root-cause analysis Medium Yes
Documents edited by AI Product Data Scientist, Data Scientist, Applied Scientist Probability theory Bayes rule, Conditional probability Easy No
Monty Hall Product Data Scientist, Data Scientist, Applied Scientist Probability theory Bayes rule, Conditional independence, Prior evidence Medium Yes
Red and blue balls Product Data Scientist, Data Scientist, Applied Scientist Probability theory Counting, Combinations, Repetition, Binomial Easy Yes
Trailing by two: should we go for two or three? Product Data Scientist, Data Scientist, Applied Scientist Probability theory Independence, Decision making Easy Yes
Two children I Product Data Scientist, Data Scientist, Applied Scientist Probability theory Prior evidence Easy Yes
Artist maxranks Product Data Scientist, Data Scientist SQL Join Easy No
Histogram of songs Product Data Scientist, Data Scientist SQL Recursive CTE, Join Hard No
Songs that did not enter the charts or entered high Product Data Scientist, Data Scientist SQL Subquery, Join Medium No
Songs that stay in the chars for a while Product Data Scientist, Data Scientist SQL Subquery, CTE, Join, ALL, Window functions Medium No
Gambler’s ruin win probability Product Data Scientist, Data Scientist, Applied Scientist Statistics Gambler ruin, Random walk, Expectation Medium Yes
Manual estimation of flips Product Data Scientist, Data Scientist, Applied Scientist Statistics Normal, CDF, Binomial, CLT Medium No
Measuring sticks Product Data Scientist, Data Scientist, Applied Scientist Statistics Variance Medium Yes
Monotonic draws Product Data Scientist, Data Scientist, Applied Scientist Statistics Expectation Hard No
Relationship between p-val and confidence interval Product Data Scientist, Data Scientist, Applied Scientist Statistics Confidence interval, P-value, Hypothesis testing Easy Yes
Two-sample t-test Product Data Scientist, Data Scientist, Applied Scientist Statistics Hypothesis testing Easy Yes
Choose a project Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Technical deep dive Discussion prep Hard Yes
Questions worksheet (deep dive) Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Technical deep dive Discussion prep Hard Yes
   Interview success probability Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Application process Application prep Easy No
   Offer validity and cooling-off periods Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Application process Application prep Easy No
   Resume review Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Application process Application prep, Resume Easy No
   Up-level during interview Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Application process Application prep Easy No
   AUC ROC and predicted output transformations Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning AUC ROC Easy Yes
   Simulate dynamic coin flips Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Coding Simulation Easy Yes
   Encoding categorical features Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Categorical features, Embeddings, One-hot encoding, Hashing Easy Yes
   Example of high-bias and high variance Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Bias-variance tradeoff Medium Yes
   Range of R2 when combining regressions Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Linear regression, Goodness of fit, R-squared, Correlation Medium Yes
   Games between two players Product Data Scientist, Data Scientist, Applied Scientist Probability theory Recursive relationship Medium Yes
   Sum of normally distributed random variables Product Data Scientist, Data Scientist, Applied Scientist Probability theory Normal, PDF, CDF Medium Yes
   Unfair coin probability Product Data Scientist, Data Scientist, Applied Scientist Probability theory Bayes rule, Conditional probability Easy No
   Choose house or techno Product Data Scientist, Data Scientist SQL Logical OR Easy No
   Expensive house songs I Product Data Scientist, Data Scientist SQL Subquery, CTE, Join Medium No
   Expensive house songs II Product Data Scientist, Data Scientist SQL Subquery, CTE, Join, Window functions Hard No
   Songs that ranked 1 to 50 Product Data Scientist, Data Scientist SQL Between Easy No
   Biased coin Product Data Scientist, Data Scientist, Applied Scientist Statistics Expectation, CLT, Binomial, Normal, Bernoulli, Hypothesis testing, CDF Medium Yes
   Prussian horses Product Data Scientist, Data Scientist, Applied Scientist Statistics Poisson, Hypothesis testing, CDF Medium Yes
   AA tests Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Variance Easy Yes
   Counterfactual definition Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Counterfactual Easy Yes
   Equal-sized treatment and control groups Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Power, Variance, Sample size Medium Yes
   False discovery control Product Data Scientist, Data Scientist, Applied Scientist A/B Testing False discovery rate, Multiple hypotheses testing, Benjamini & Hochberg, Bonferroni Easy Yes
   Interference Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Interference Easy Yes
   Multi-armed and contextual bandits in AB testing Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Contextual bandits, Multi-armed bandits Medium Yes
   Novelty and primacy effects Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Novelty effects, Primacy effects Easy Yes
   Randomization checks Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Randomization Easy Yes
   Randomization level Product Data Scientist, Data Scientist, Applied Scientist A/B Testing Randomization, Variance Medium Yes
   Bayesian Frequentist statistics Applied Scientist, Machine Learning Engineer Machine Learning Bayesian, Frequentist Easy Yes
   Bias-variance biased estimator Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Bias-variance tradeoff Medium Yes
   Bootstrap Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Bootstrap Easy Yes
   K-means from scratch Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Coding k-means Medium Yes
   Linear regression with stochastic gradient descent Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Coding Stochastic Gradient descent, Linear regression Medium Yes
   Logistic regression with gradient descent Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Coding Gradient descent, Logistic regression Medium Yes
   Naive Bayes from scratch Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Coding Gaussian Naive Bayes Medium Yes
   Principal Component Analysis (PCA) from scratch Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Coding Principal Component Analysis (PCA) Medium Yes
   Common causes of data leakage Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Data leakage Medium Yes
   Comparing decision trees with random forests Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Decision trees, Random forests Easy Yes
   Correlation with binary variables Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Correlation, Hypothesis testing, Point-biserial correlation coefficient Easy No
   Cross validation Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Cross validation, Offline evaluation Easy Yes
   Decide between a multinomial vs. a binary modeling approach Applied Scientist, Machine Learning Engineer Machine Learning Modeling, Multinomial, Binary Easy Yes
   Discretization drawbacks Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Categorical variables, Discretization Easy Yes
   Exponentially weighted moving average Applied Scientist, Machine Learning Engineer Machine Learning Exponentially weighted moving average, Formula derivation, Proof Medium No
   Feature engineering in the era of deep learning Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Feature engineering Easy Yes
   Gradient boosting vs. random forests Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Gradient boosting, Random forests, Bagging, Boosting Medium Yes
   Gradient descent vs. Stochastic Gradient descent and local minima Applied Scientist, Machine Learning Engineer Machine Learning Gradient descent (GD), Stochastic gradient descent (SGD), Local minima, Optimization, Deep learning Medium Yes
   Gradient descent vs. Stochastic Gradient descent and learning rate Applied Scientist, Machine Learning Engineer Machine Learning Gradient descent (GD), Stochastic gradient descent (SGD), Learning rate, Optimization, Deep learning Medium Yes
   Hypothesis testing in regression coefficients Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Linear regression, Hypothesis testing Medium No
   Intercept Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Linear regression, Intercept Easy No
   Interpretability Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning ML interpretability Easy Yes
   Linear regression assumptions Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Linear regression Easy Yes
   Linear regression with duplicated rows Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Linear regression, Statistical significance Easy Yes
   Logistic regression and standardization Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Logistic regression, Standardization Easy Yes
   Logistic regression assumptions Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Logistic regression Easy Yes
   Missing data Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Missing data Easy Yes
   MSE vs. MAE Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning MSE, MAE Easy Yes
   Multicollinearity Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Multicollinearity, Linear regression Medium Yes
   Non-probability sampling Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Sampling, Non-probability Easy Yes
   Normalization vs. Standardization Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Linear regression, Standardization, Normalization Easy Yes
   Not enough data to train a model Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Data limitations Easy Yes
   Outliers Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Outliers, Cook’s distance, Regularization Easy Yes
   Principal Component Analysis (PCA) Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning PCA Easy Yes
   Prove that a median minizes MAE Applied Scientist, Machine Learning Engineer Machine Learning MAE, Median, Formula derivation, Proof Hard Yes
   Random vs. stratified sampling Product Data Scientist, Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Sampling, Stratified sampling Easy Yes
   SMOTE Applied Scientist, Machine Learning Engineer Machine Learning Imbalanced classification, SMOTE, Data augmentation Easy Yes
   Weighted and importance sampling Data Scientist, Applied Scientist, Machine Learning Engineer Machine Learning Sampling, Weighted sampling, Importance sampling Easy No
   Characteristics of metrics Product Data Scientist, Data Scientist Metrics Characteristics of metrics Easy Yes
   Types of metrics Product Data Scientist, Data Scientist Metrics Types of metrics Easy Yes
   Consecutive tails Product Data Scientist, Data Scientist, Applied Scientist Probability theory Permutations, Repetition Easy No
   Largest number rolled Product Data Scientist, Data Scientist, Applied Scientist Probability theory Counting, Permutations, Repetition Medium Yes
   Median probability Product Data Scientist, Data Scientist, Applied Scientist Probability theory Binomial, Uniform, CDF Medium Yes
   Number of emails Product Data Scientist, Data Scientist, Applied Scientist Probability theory Poisson distribution Easy No
   Paths to destination Product Data Scientist, Data Scientist, Applied Scientist Probability theory Counting, Combinations Easy No
   Repeated rolls until 4 Product Data Scientist, Data Scientist, Applied Scientist Probability theory Geometric distribution Easy Yes
   Sample digits 1-10 Product Data Scientist, Data Scientist, Applied Scientist Probability theory Sample from samples, Uniform Medium Yes
   Two fair die rolls Product Data Scientist, Data Scientist, Applied Scientist Probability theory Independence, CDF, PMF Easy No
   Artists with more songs than others Product Data Scientist, Data Scientist SQL Subquery, CTE, Join, Window functions Hard No
   Concat columns Product Data Scientist, Data Scientist SQL Concat Easy No
   Label recent songs Product Data Scientist, Data Scientist SQL Case Easy No
   Median songs per artist Product Data Scientist, Data Scientist SQL CTE, Window functions Hard No
   Songs in charts with greater durations Product Data Scientist, Data Scientist SQL Subquery, CTE, Join, Window functions Hard No
   Songs per genre Product Data Scientist, Data Scientist SQL Group by Easy No
   Songs with letters Product Data Scientist, Data Scientist SQL Regexp Easy No
   An intuitive way to write power Product Data Scientist, Data Scientist, Applied Scientist Statistics Power, Hypothesis testing Easy Yes
   Buy and sell stocks Product Data Scientist, Data Scientist, Applied Scientist Statistics Gambler ruin, Expectation, Recursion, Random walk Medium Yes
   CI of flipping heads Product Data Scientist, Data Scientist, Applied Scientist Statistics Confidence Interval, CLT, Bernoulli trials Medium Yes
   Confidence interval definition Product Data Scientist, Data Scientist, Applied Scientist Statistics Confidence interval, Hypothesis testing Easy Yes
   Confidence intervals that overlap Product Data Scientist, Data Scientist, Applied Scientist Statistics Confidence interval, Hypothesis testing Medium No
   Covariance of dependent variables Product Data Scientist, Data Scientist, Applied Scientist Statistics Variance, Uniform, Covariance, Expectation Medium Yes
   Distribution of a CDF Product Data Scientist, Data Scientist, Applied Scientist Statistics CDF, Inverse transform Medium No
   Dynamic coin flips Product Data Scientist, Data Scientist, Applied Scientist Statistics Expectation, Simulation Hard Yes
   Expected number of consecutive heads Product Data Scientist, Data Scientist, Applied Scientist Statistics Expectation Medium Yes
   Number of draws to get greater than 1 Product Data Scientist, Data Scientist, Applied Scientist Statistics Normal, Geometric, CDF, Expectation Medium Yes
   P-value definition Product Data Scientist, Data Scientist, Applied Scientist Statistics P-value, Hypothesis testing Easy Yes
   Tests for normality Product Data Scientist, Data Scientist, Applied Scientist Statistics Hypothesis testing, Normality Easy Yes