Tiktok Data Scientist Machine Learning Interview Questions
Practice 19 real Machine Learning interview questions for Data Scientist roles at Tiktok.
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Design multimodal deployment under compute limits
You need to answer a set of questions related to multimodal model deployment and post-training optimization in an interview. Provide systematic explan...
Explain and tune XGBoost; prevent overfitting
XGBoost Tree Booster: Objective, Hyperparameters, Tuning for Imbalanced Detection, and Post-training Use Context: You are building a binary classifier...
How would you manage precision/recall for fraud detection?
Scenario You own (or significantly contribute to) a production fraud detection system that flags transactions/users as fraud vs legit. - The model out...
Design recommendations objective balancing growth and monetization
Design a Multi-Objective Recommender for Long-Form Content You are designing the ranking objective and measurement plan for a long-form content recomm...
Detect and suppress bad sellers robustly
System Design: Identify and Suppress Bad Sellers in a Commerce Marketplace Context You are designing an ML-driven risk system for a large-scale market...
Explain SHAP vs VIF under collinearity
High Collinearity in Binary Classification: VIF, SHAP, and Interpretation Strategy You are modeling a binary outcome Y. Two numeric features A and B a...
Contrast LSTM and Transformer for long sequences
Train a Long-Context Autoregressive LM (T = 8192, H = 512, B = 8) You are training an autoregressive language model with: - Sequence length T = 8192 t...
How do you choose a classification threshold?
Context You built a binary sentiment classification model (e.g., positive vs. negative) and need to deploy it in a product where actions depend on the...
Choose linear regression or decision tree appropriately
Choose Between Linear Regression and a Decision Tree Under a Hinge and Interaction DGP Context You have 100,000 i.i.d. observations with features x1 (...
Compare bagging vs boosting on imbalanced data
Fraud Detection on 10M Time-Ordered Transactions (0.5% Fraud) You are building a binary classifier to detect 0.5% fraudulent events among 10,000,000 t...
When prioritize precision vs recall
Context You are working on a product team and building (or evaluating) a binary classifier that triggers an action (e.g., show a warning, block conten...
Design an ad-selection system across objectives
End-to-End Ad-Selection System Design Context You must choose, at impression time, which advertiser type to show to a user. There are three advertiser...
Estimate heterogeneous treatment effects with causal ML
Context You are given large-scale, logged observational data from an always-on promotion. Each record contains features X (user/context), a binary tre...
Predict User Churn with Effective Modeling Techniques
Predicting User Churn for a Subscription App Context You are building a model to predict which active subscribers are likely to churn soon so the team...
Compare Random Forests and Boosted Trees: Bias, Variance, Speed
Scenario A product/data science team is deciding between Random Forests and Gradient-Boosted Decision Trees (e.g., XGBoost) for a new predictive task....
Design Real-Time Credit Card Fraud Detection System
Real-Time Credit-Card Fraud Detection System Design Scenario You are designing a real-time fraud detection system for an online payments platform that...
Predict Customer Churn with Machine Learning Workflow
Predicting Monthly Churn: End-to-End Workflow Scenario A subscription platform wants to predict whether a customer will churn in the next month. Assum...
Personalize Ad Delivery Using Machine Learning Techniques
Personalized Delivery of Three Ad Categories Scenario You operate a consumer feed with a single ad opportunity per request and three possible ad categ...
Choose Between Random Forests and Gradient Boosting Models
Scenario Product-facing data science interview on choosing and configuring tree-based ensemble models for tabular prediction in a production setting. ...