I had an interview a while ago. Two Sigma 的 Data Scientist 岗位，趁着记忆还比较新鲜，来跟大家详细分享一下整个流程和 Two Sigma interview questions 体验。Two Sigma 在量化和数据科学圈子里算是顶尖的存在，整个面试过程对数据处理、统计建模和思维逻辑的要求都比较高，整体偏严谨但不刁钻。
OA Written examination phase
Two Sigma's OA is not like other companies' LeetCode brushing, the kind of pure algorithm coding questions are rare here. It's more like cleaning, modeling and analyzing that data scientists do everyday.
OA1: Data Processing + Feature Engineering
The question gives a dirty dataset: it has both missing values and extreme outliers. Requirement:
- Clean up missing values (different columns in different ways);
- Generating new features;
- Give the results of the underlying statistical analysis.
When I started writing the code, I used to just drop the missing values. The on-line helper immediately warned me, "Careful, that might be too much information to lose, do you want to consider filling in with the mean/median, or splitting it up?" This comment woke me up and made me write the explanatory note. This proved to be a plus, and the interviewer at the VO later made a point of asking:
"Why did you choose this filler at the time? How would you have handled it if there was a high percentage missing?"
Thanks to a reminder earlier in the thread, I can explain the logic right away, or I'd likely get stuck.
OA2: regression + time series + bonus questions
This set is more on the modeling side:
- The first question is on the basics of linear regression, examining the explanatory coefficients and the effect of the fit.
- The second question was a time series analysis (similar to the NYC temperature dataset) with smoothing and trend forecasting.
- The third question is bonus, which can be used feature selection The idea of the solution.
When I wrote about the regression question, the on-line helper popped up again, "Don't forget to write down the hypothetical conditions and the interpretation of the results, don't just give the values." Sure enough, the hiring manager followed up with a question during the phone interview:
"If we double the data, will beta and R² change? what happens to the t-statistics?"
Since I was prepared before, I gave the intuition first: the coefficients and R² remain unchanged, but the t-statistics are scaled up. Then I added the formula. The interviewer nodded obviously and the atmosphere was smooth.
Phone Interview
The phone interview was conducted by the Hiring Manager. The first half of the interview was about my experience and motivation, and I was asked why I wanted to come to Two Sigma and what data projects I had done before. There were no traps here, it was mainly about communication.
The technical part throws in that "regression after replication" question. It's really a test of your statistical intuition rather than memorization of formulas.
After I answered, he followed up with a follow-up question:
"If you don't just replicate, but add noise, how does that affect the regression results?"
Luckily, the assisting team had already hinted at the "possible chasing of data distribution changes" when I answered the first question, so I smoothly picked up on it and explained that the noise would cause the standard error to become larger, thus reducing the significance. The logic chain of the answer was sort of complete.
Virtual Onsite
VO Three rounds of technical interviews in total, all in one morning, very fast paced.
Round 1: Coding
I was given a dataset and asked to write a pipeline in Python/pandas to do the aggregation and cleaning, as well as to handle boundary cases. Halfway through writing I forgot to consider null inputs, and the helper immediately reminded me, "Make up an if not df.empty, or the test case will blow up."
The interviewer did ask at the end:
"What does your function return if the input is an empty table?"
I answered it with no problem, sort of stepping over the pitfalls early.
Round 2: Case Study - Alternative Data
The title of this round is:Predicting e-commerce sales with search data.
At first I said I could treat the search volume as a feature and build a regression model directly. The interviewer immediately followed up:
"Search data can be seasonal and trending, what are you going to do with it?"
At this time the on-line helper reminded in the headset: "plus seasonality adjustment, you can mention the difference or add the time dummy." I obediently added the time series features and feature selection, and the effect is very good.
Overall, I feel that this round is not about how fancy the model is, but about the clarity of thought. It's a plus if you can explain why you chose those features and what to do next if the results are not favorable.
Round 3: Case Study - Credit Card Transaction Analysis
The title is. Detecting Repeat Credit Card Transactions.
- I started with a plain solution: match by transaction_id, amount and timestamp.
- The interviewer goes on to ask: "What if the same user does swipe twice in a short period of time? Does that still count as a duplicate?"
I was a bit hesitant at one point, and the assist immediately reminded, "Emphasize the need to set threshold intervals and also consider the angle of fraud detection."
So I added: to distinguish between misuse vs. system error vs. fraud, you can combine rule engine + machine learning model. The interviewer was very satisfied with this answer, and even started to talk about risk management.
Overall Impression
Two Sigma questions are more like academic discussions than rote memorization. Interviewers often follow up with "what if the situation changes", which tests reaction and logic.
Technically, you need to be able to write the right code as well as explain why you wrote it, combining intuition with formulas.
Case Study especially emphasizes communication skills, you have to dare to ask questions and discuss the scenarios and assumptions clearly.
With the on-line assistants beside the voice reminders, it really is a lot more stable, a lot of details of the pitfalls are circumvented in advance, so as not to panic when the interviewer pursued the question.
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To be honest, I was able to get through this time thanks to programhelp's real-time voice reminders. There were many places where I would have gotten stuck if I had been on my own, but when someone nudged me at a critical moment, the whole answer was complete. For students who want to hit the quantitative and large factories, it is highly recommended not to carry on alone.
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