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Interview questions by company and topic | SysRound

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63 questions

Showing 1–25

Machine learningHard
Train a classifier and analyze dataset
Companies: OpenAI
Model Evaluation · Supervised Learning
Last reported Sep 2025
Open workspace
Machine learningHard
Address Missing Income Bracket in California Housing Data
Companies: Upstart
Feature Engineering · Supervised Learning
Open workspace
Machine learningMedium
Analyze CTR Data and Train Model
Companies: Reddit
CTR Prediction & Ads ML · Supervised Learning
Open workspace

Page 1 of 3

Machine learningHard

Build a Heart Disease Baseline

Companies: Hudson River Trading

Data Cleaning & Wrangling · Supervised Learning

Machine learningHard

Build a late-delivery risk model

Companies: DoorDash

Feature Engineering · Supervised Learning

Machine learningMedium

Build a model to predict wine quality

Companies: Evenup

Feature Engineering · Supervised Learning

Machine learningEasy

Build a predictive model from TurboTax sample data

Companies: Intuit

Feature Engineering · Supervised Learning

Machine learningMedium

Build and evaluate an order prediction model

Companies: Airbnb

Model Evaluation · Supervised Learning

Machine learningMedium

Build Churn Prediction and Survival Models

Companies: Gusto

Regression & Statistical Modeling · Supervised Learning

Machine learningEasy

Build House Price Model Responsibly

Companies: Capital One

Fairness & Bias · Supervised Learning

Machine learningMedium

Build leak-safe sklearn model with calibration

Companies: Apple

Feature Engineering · Supervised Learning

Machine learningMedium

Build Model to Predict Customer Contract Renewal

Companies: Google

Feature Engineering · Supervised Learning

Machine learningMedium

Can One k Always Maximize kNN Leave-One-Out Accuracy?

Companies: C3 Ai

Model Evaluation · Supervised Learning

Machine learningMedium

Choose a Parameter to Reduce Underfitting in Gradient-Boosted Trees

Companies: C3 Ai

ML Fundamentals · Supervised Learning

Machine learningHard

Choose Labels and Losses for Multiple Engagement Outcomes

Companies: Snapchat

Model Evaluation · Supervised Learning

Machine learningHard

Choose Models for Imbalanced Data and Time-Series Forecasting

Companies: Amazon

Supervised Learning · Time Series & Forecasting

Machine learningMedium

Compare How Models Respond to Multicollinearity

Companies: C3 Ai

Regression & Statistical Modeling · Supervised Learning

Machine learningMedium

Compare Logistic Regression and Random Forest in Limited Data Scenarios

Companies: Google

ML Fundamentals · Supervised Learning

Machine learningMedium

Compare Logistic Regression and Random Forest in Python

Companies: Capital One

Model Evaluation · Supervised Learning

Machine learningMedium

Compare Random Forests and Boosted Trees: Bias, Variance, Speed

Companies: TikTok

Supervised Learning

Machine learningMedium

Construct a Churn-Prediction Pipeline Using Scikit-Learn

Companies: Apple

Feature Engineering · Supervised Learning

Machine learningEasy

Decision Trees in Mathematical Terms, and What Random Forests and XGBoost Improve

Companies: Squarepoint

ML Fundamentals · Supervised Learning

Machine learningMedium

Deep-dive XGBoost handling and overfitting

Companies: Capital One

Model Evaluation · Supervised Learning

Machine learningEasy

Derive logistic regression objective and gradients

Companies: Upstart

ML Fundamentals · Supervised Learning

Machine learningHard

Design classification under missingness and imbalance

Companies: Cvs Health

Model Evaluation · Supervised Learning