Two Sigma Machine Learning Interview Questions
Practice the exact questions companies are asking right now.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"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."
Analyze Temperatures and Update Regression
You are given historical daily temperature data for New York City and several nearby towns. Each row contains a date, the NYC temperature, and the tem...
Predict bike demand and avoid overfitting
You are given historical data for a city bike-sharing system. Available fields include station_id, hourly timestamp, number of bike pickups and return...
How would you forecast bike demand?
This question evaluates a candidate's competency in time-series forecasting, feature engineering, model selection, and evaluation for short-horizon de...
Predict Bike Dock Demand
This question evaluates a data scientist's competency in time-series demand forecasting, covering temporal target definition, temporal feature enginee...
Design features for house price prediction
This question evaluates competence in feature engineering, data preprocessing, baseline regression modeling, and model evaluation for tabular price pr...
Build a Housing Price Prediction Model from Historical Data
You are given a historical dataset of residential real-estate transactions — sale records with property attributes, location information, and sale dat...
How detect duplicate card records?
You are given a dataset of credit card transaction records and suspect that some records are duplicates. Discuss: - What real-world situations could c...
Daily Temperatures by Town: Linear Models to Predict NYC Temperature
Daily Temperatures by Town: Linear Models to Predict NYC Temperature You are given a dataset of historical daily temperature readings for New York Cit...
Perform no-intercept linear regression from two datasets
You are given two pandas datasets to fit an OLS model without an intercept (through origin). Dataset A (features): df_X(user_id, clicks, impressions)....
How to forecast bike dock demand
This question evaluates competency in time-series forecasting and demand prediction, including feature engineering, model selection and evaluation, ha...
How Would You Test Whether Momentum Is a Real Alpha Signal in Stocks?
In a quantitative-research data-analysis round, you are asked a methodology question. No coding is required — the interviewer wants you to reason like...
Design a house-price prediction model
This question evaluates machine learning and data science competencies including regression model design, feature engineering, data-splitting and leak...
Predict a Company's Stock Price from Six Years of Web Search Logs
You are given a DataFrame containing the past six years of web search logs. Each row is a single search event with the following columns: | Column | T...
Slopes of y-on-x vs x-on-y Regression: Relationship and When They Are Equal
You fit two simple ordinary least squares (OLS) regressions, each with an intercept, on the same paired dataset $(x_i, y_i)$ for $i = 1, \dots, n$: 1....
Forecast bikes available at a station
This question evaluates proficiency in time-series forecasting, feature engineering, handling temporal data splits, and incorporating operational cons...
Predict Stock Prices from Google Search Data
You are given access to historical Google search data — relative search volume over time for arbitrary query terms — along with standard historical ma...
Derive correlation bounds and omitted-variable bias
This question evaluates understanding of multivariate correlation structure and linear regression properties, focusing on feasible ranges and construc...
Why Is the Sample Mean Approximately Normal for Large Samples?
In statistics we routinely treat the average of a large sample as if it were normally distributed — for example, when building confidence intervals fo...