Honestly.Jane Street It was the longest and most challenging interview I've ever prepared for, and unlike traditional tech companies, this fintech company has a lot of PhDs in math, physics and computer science. Unlike traditional tech companies, this fintech company has a lot of PhDs in math, physics, computer science, and even researchers who directly transitioned from academia, and the biggest feeling I had throughout the interview was that the questions were very hardcore, the thinking was very in-depth, and the pace was very fast. Programhelp full range of voice assistance, in order to successfully hold each round.
Overview of the interview process
The overall process has three levels:
- Application Review: I received an invitation for an interview about a week or so after submitting my resume.
- Telephone interviews (2-3 rounds): Each round is about 30-60 minutes long and consists mainly of technical brain teasers + modeling questions.
- Full day VO (4-6 rounds)The program includes live coding, probabilistic reasoning, trading strategy design, etc. It's extremely brain-intensive.
Round 1: Probability + Strategy Questions
The first question caught me off guard (thankfully the voice assist reminded me of the idea in time):
"You have a 20-sided die with numbers from 1 to 20 that you can roll up to 100 times, and each time you can choose to either roll and continue, or take the current number as the payoff. Design a strategy to maximize the expected payoff."
I started out by saying to set the threshold, say 15 or more to take, but the interviewer immediately followed up with, "Why 15 and how did you figure it?"
Programhelp's voice-assist helped me with a timely tip on calculating the balance between expectations and average roll counts. I did the math on the spot:
- Let threshold be T and the expected return be the average of T~20;
- But the probability that roll reaches this range is (21-T)/20;
- Combined expectation = [T+20]/2 × (21-T)/20
I tried some values of T and found that T=11 has the highest time horizon gain, so I came up with the strategy: take when the number ≥11, otherwise keep rolling.
The interviewer is satisfied and then changes the rules:
"Now every time you take the dice automatically re-roll. how do you change the strategy?"
This turned into a multi-round game problem. I quickly reset the expectation calculation based on the voice alert, treating each roll + take combination as a separate event, and re-arriving at a threshold of about 13.
Then the next question is even more hardcore:
"You and the casino are in a game: you can choose to take and the casino can choose to reroll. analyze the equilibrium strategy."
This is pure game theory. I quickly built a payoff matrix with the help of a voice guide, analyzed the equilibrium point between my payoff and the casino's payoff based on the Nash equilibrium idea, and finally estimated that the threshold should be around 10.
Round 2: Market Making + Python Strategy Implementation
The topics are a little bit more relatively engineered:
"You are the market maker and need to give buy and sell quotes based on historical data while controlling inventory risk."
My first thought was: the higher the volatility, the wider the spread, and if the inventory is skewed to one side, the higher the price in that direction. So I quickly wrote a logic demo in Python (the voice reminds me of the keywords "mean reversion" and inventory-adjusted spread):
def calculate_quotes(current_price, inventory, volatility, max_inventory).
base_spread = volatility * 2
inventory_adjustment = (inventory / max_inventory) * 0.01
bid = current_price - base_spread/2 - inventory_adjustment
ask = current_price + base_spread/2 - inventory_adjustment
return bid, ask
Although the code is not the final version, but the logic expressed clearly passed. After that, we also discussed how to dynamically adjust the parameters, strategy adjustments in extreme market conditions, etc.
Final Round: Mathematical Depth + High Frequency Trading Modeling
Quantitative researcher is on the scene and the questions are immediately pulled full of difficulty.
Bayesian inference questions
"Two boxes, one with two white balls and one with one white and one black, you pick one at random and draw a white ball. What is the probability that this ball came from which box?"
This is a typical Bayesian question and I write it on the spot:
- P(two white boxes | white ball) = P(white | two white boxes) × P(two white boxes) / P(white)
- = (1 × 0.5) / (0.5 × 1 + 0.5 × 0.5) = 2/3
Order Book Data Structure Design Questions
"Design an order book that supports insertion, deletion, and querying for optimal prices."
I chose to use max heap + min heap to maintain bid and ask respectively, and then use hashmap to record the price-quantity correspondence, with O(log n) for insertion and deletion and O(1) for querying. After analyzing the complexity, we also discuss how to optimize in HFT environment, including memory pool, timestamp consistency, etc.
Statistical Arbitrage Strategy Questions
"How do you capture this arbitrage opportunity when there is a price deviation between two stocks?"
The voice assistants prompted the key concept of "cointegration" in advance. My complete process was:
- Test for cointegration with historical data;
- Sets the entry/exit threshold;
- Consider transaction cost and slippage;
- Set wind control parameters: position sizing, stop loss, drawdown limit.
Behavioral interviews aren't easy either
The last few rounds are standard behavioral:
Why did you switch to quant?
Describe an experience of making a decision under high pressure?
Ever had a particularly complex problem and how did you solve it?
My answer combined my internship experience (I also went through the Programhelp simulation in advance), clear logic + real reflection, and it worked well.
summarize
The interview at Jane Street was really not easy, especially the probability, strategy design, data structure and game theory questions, which were far more intense than most tech companies. Good thing I prepared long enough + Programhelp The VO voice assistant was with me the whole time and helped me a lot from modeling ideas to interview pacing.
If you are also planning to hit the quant / trading / algo direction, Jane Street is one of those challenges that can't be missed!
