Quant interviews test probability, expected value, statistics, mental math, brain teasers and coding, plus market-making and betting games for trading roles and behavioral questions for every role. Interviewers grade how you reason out loud as much as your final number. To prepare, practice every day: timed mental math, probability problems, coding on LeetCode and HackerRank, and behavioral answers said out loud.
I'm Fiona Taft, founder of QuantMinds, former Executive Director of UC Berkeley's MFE program and former Associate Director at Stanford University. The guidance here is drawn from my recorded webinars on quant recruiting. Quotes from my webinars are lightly edited for clarity. The 19 practice questions are original, with worked answers, and firm details come only from each firm's own careers pages.
The quant interview process
Most firms run a version of these five stages, covered in more detail in our guide on how to become a quant.
- Resume screen. A clean format that shows quickly who you are. See why your quant resume isn't getting responses.
- Online assessment (OA). A timed technical test.
- HR interview. Some firms hold it before the OA, and some use a recorded video interview such as HireVue.
- Technical interviews. Live problem solving, plus questions about your projects.
- Superday. A final round of several interviews, online or in person.
In my AMA on quant campus recruiting, I described the OA this way: "The next step in the process is going to be an online assessment, a technical assessment. This is going to be anywhere from mental math to probability, stats and coding. Usually, HackerRank is what most of the firms are using."
In my webinar Are You Behind the Curve for Full-Time Graduate Recruiting?, I added: "You're going to get that assessment, and you're going to need to pass it. And not just pass, but score high on it for firms to ask you for the next step, which is the HR interview." I put the interviews at about 4 to 8 weeks for an internship and 6 to 8 weeks for a graduate role.
What quant interview questions test
- Probability: counting outcomes, conditional probability and Bayes' rule.
- Expected value: the fair value of a game, when to stop, and linearity of expectation.
- Statistics: standard errors, regression, and telling signal from noise.
- Brain teasers: structured reasoning on an unfamiliar puzzle.
- Mental math: fast, accurate arithmetic under time pressure.
- Market making and betting: a fair value, where you would buy and sell, updating on new information, and bet sizing.
- Coding: data structures, algorithms, the time and memory cost of your approach, and edge cases.
- Behavioral: your resume and projects, teamwork, why this firm, and what you read to follow markets. Firms also check whether you can explain technical work to people who are less technical.
How QR, QT and QD interviews differ
The topics overlap, but the weight shifts by role. If you have not picked one, read QR, QT or QD: which quant path is right for you.
- Quant researcher (QR): heaviest on probability, statistics and modeling. I match math- and stats-heavy profiles to research, and Jane Street says its research interviews are a mix of its trading and software engineering interviews.
- Quant trader (QT): speed under pressure. I list handling high-pressure situations and doing mental math quickly as trading skills.
- Quant developer (QD): coding first. Jane Street says its software engineering interviews work through coding challenges.
For trading roles, I said in the same campus recruiting AMA: "If you're applying to the market-making firms, you're going to want to be studying poker, you're going to want to be studying market making, and so forth." On languages, in my Quant Finance Career AMA, I said: "If you are targeting quant researcher roles, you're going to need Python, and you're going to need libraries such as NumPy and others. If you're going to be more on the quant dev side, then you're going to need more languages in your repertoire." For the math bar by role, read the math you actually need to break into quant finance.
How to answer in the room
In the Quant Finance Career AMA, I explained what interviewers watch: "When they ask you technical questions in your interviews, they are trying to judge your way of thinking. How are you solving the problem? And is that person open to feedback? For instance, if you're going down the wrong path, someone might say, you might want to think about this problem this way."
- Think out loud. Firms want clear, structured thinking under pressure, and an interviewer can only credit reasoning they hear.
- State your assumptions. Ask clarifying questions first, then say what you are assuming, such as fair dice or independent events.
- Estimate, then refine. A rough answer lets you sanity-check the exact one.
- Check edge cases, such as duplicates, empty inputs and ties.
- Take the hint. If the interviewer suggests another approach, try it.
- If you are stuck, say where, and what you would try next.
Practice questions with worked answers
Each technical question carries a difficulty label. Try it on paper first, say your reasoning out loud as you would in an interview, then check the worked answer.
Quant probability questions
Q1. Two eight-sided dice (Probability, Easy)
You roll two fair eight-sided dice, numbered 1 to 8. A friend looks and tells you only that the total is at least 13. What is the probability that at least one of the dice shows an 8?
Answer: 7/10. There are 64 equally likely ordered outcomes. Totals of 13, 14, 15 and 16 come from 4, 3, 2 and 1 ordered pairs, so 10 outcomes meet the condition. The ones containing an 8 are (5,8), (8,5), (6,8), (8,6), (7,8), (8,7) and (8,8), which is 7 outcomes. Every outcome in the reduced list is still equally likely, so the answer is 7 out of 10.
What they are checking: Whether you shrink the set of possible outcomes correctly when given information, and count ordered pairs instead of guessing.
Q2. A backtest that passes (Probability, Easy)
A research team estimates that 2% of the strategy ideas it tests have a real edge. Its backtest filter passes 90% of the strategies that have a real edge, and it also passes 10% of the strategies that have none. A new strategy passes the filter. What is the probability that it has a real edge?
Answer: 9/58, about 15.5%. Picture 1,000 ideas: 20 have an edge, and 18 of those pass. Of the 980 with no edge, 98 pass. So 116 ideas pass in total, and 18 of them are real, giving 18/116 = 9/58. When real edges are rare, even a filter that sounds accurate mostly lets through false positives.
What they are checking: Bayes' rule, and whether you respect the base rate (how common real edges are to begin with) instead of reading 90% as the answer.
Q3. Reaching a target before going broke (Probability, Medium)
You start with $3. Each round you win $1 with probability 0.6 or lose $1 with probability 0.4, independently of earlier rounds. You stop as soon as you have $6 or $0. What is the probability that you finish with $6?
Answer: 27/35, about 77.1%. Let P(k) be the probability of reaching $6 starting from $k. Conditioning on the next round gives P(k) = 0.6 P(k+1) + 0.4 P(k-1), with P(0) = 0 and P(6) = 1. The solution is P(k) = (1 - rᵏ) / (1 - r⁶) with r = 0.4/0.6 = 2/3; you can check that it satisfies the equation and both end conditions. Because 1 - r⁶ = (1 - r³)(1 + r³), starting from $3 simplifies to P(3) = 1 / (1 + r³) = 1 / (1 + 8/27) = 27/35.
What they are checking: Setting up a recursion by conditioning on the first step, using the boundary conditions, and spotting a simplification instead of grinding through arithmetic.
Expected value questions
Q4. Adding rolls until a six (Expected Value, Easy)
You roll a fair six-sided die over and over, adding each result to a running total. The moment you roll a 6 the game ends, and that 6 is not added. What is the expected final total?
Answer: 15. Let E be the expected total. On the first roll, with probability 1/6 you roll a 6 and finish with 0. With probability 5/6 you roll one of 1 to 5, which averages 3, and then the game effectively starts over, so you expect 3 + E from that point. That gives E = (5/6)(3 + E), so E/6 = 5/2 and E = 15. A second route: you expect 5 non-six rolls before the first 6, each averaging 3, and 5 × 3 = 15.
What they are checking: Writing a first-step equation for an expectation, and noticing that a roll known not to be a 6 averages 3, not 3.5.
Q5. Orders spread across venues (Expected Value, Medium)
A trading desk sends 5 orders. Each order is routed independently and uniformly at random to one of 10 trading venues. What is the expected number of distinct venues that receive at least one order?
Answer: 4.0951. Write the count as a sum of 10 indicators, one per venue, each equal to 1 if that venue receives at least one order and 0 otherwise. A given venue receives no orders with probability 0.9⁵ = 0.59049, so it is used with probability 0.40951. Expectations add even when the indicators depend on each other, so the answer is 10 × 0.40951 = 4.0951.
What they are checking: Linearity of expectation with indicator variables, which avoids an ugly case-by-case count.
Q6. Paying to reroll an eight-sided die (Expected Value, Medium)
You roll a fair eight-sided die (1 to 8). After each roll you may either stop and take the face value in dollars, or pay $1 to roll again. You can roll at most three times in total, and if you reach the third roll you must take it. With the best strategy, what is the game worth after reroll fees?
Answer: 169/32, about $5.28. Work backwards, comparing each roll with what continuing is worth after paying the $1 fee.
- The third roll is worth its average face, 4.5.
- On the second roll, rerolling is worth 4.5 - 1 = 3.5, so keep 4 or higher. You keep 4 to 8 with probability 5/8 (averaging 6) and otherwise take 3.5, so a second roll is worth (5/8) × 6 + (3/8) × 3.5 = 5.0625.
- On the first roll, rerolling is worth 5.0625 - 1 = 4.0625, so keep 5 or higher. You keep 5 to 8 with probability 1/2 (averaging 6.5) and otherwise take 4.0625, so the game is worth 0.5 × 6.5 + 0.5 × 4.0625 = 5.28125, which is 169/32.
The strategy is: on the first roll keep 5 or higher, on the second roll keep 4 or higher. Without the fee you would hold out for 6 and then 5, so the cost of rerolling makes you less picky at every stage.
What they are checking: Backward induction for a stopping rule: compare each roll with the value of continuing after costs, not with the average roll.
Statistics questions
Q7. How much data before an edge shows up (Statistics, Easy)
A strategy has a true average return of 0.05% per day, with a daily standard deviation of 1%. Treat days as independent. Roughly how many trading days of data would it take for that true average to sit two standard errors above zero, meaning an expected t-statistic of 2?
Answer: About 1,600 days, a little over six years at 252 trading days a year. The standard error of the average after n days is 1% / √n. You want 0.05% = 2 × 1% / √n, so √n = 40 and n = 1,600. The average you actually measure is itself random, so with 1,600 days of data you would clear t = 2 only about half the time. Precision improves only with the square root of the sample size, so cutting the error in half takes four times as much data.
What they are checking: That you know how standard errors shrink with sample size, and that you have realistic expectations about how noisy daily returns are.
Q8. Two regressions on the same data (Statistics, Medium)
Using one data set, you fit two ordinary least squares regressions, each with an intercept. Regressing y on x gives a slope of 0.8, and regressing x on y gives a slope of 0.45. What is the correlation between x and y, and which variable has the larger standard deviation?
Answer: The correlation is 0.6, and y has the larger standard deviation, with sd(y)/sd(x) = 4/3. The slope of y on x equals the correlation times sd(y)/sd(x), and the slope of x on y equals the correlation times sd(x)/sd(y). Multiplying the two slopes cancels the standard deviations and leaves the correlation squared: 0.8 × 0.45 = 0.36, so the correlation is 0.6, positive because both slopes are positive. Then sd(y)/sd(x) = 0.8 / 0.6 = 4/3. The two slopes are reciprocals only when the correlation is exactly 1 or -1; otherwise both regressions pull their predictions toward the average.
What they are checking: That you know what a regression slope is made of, and that regressing in the other direction does not simply invert the slope.
Brain teasers
Q9. Finding the heavy coin (Brain Teaser, Easy)
You have 25 coins that look identical. One of them is slightly heavier and the rest all weigh the same. Using a balance scale that only tells you which side is heavier, or that the sides balance, what is the smallest number of weighings that guarantees you find the heavy coin?
Answer: 3. Each weighing has three possible outcomes (left heavier, right heavier, balanced), so two weighings can tell apart at most 3 × 3 = 9 possibilities, fewer than 25. Three weighings are enough:
- Put 9 coins on each side and leave 7 off. The heavy coin is in the heavier group of 9, or among the 7 if the sides balance.
- From 9 coins, weigh 3 against 3 to narrow it to 3 coins. From 7 coins, weigh 3 against 3: if they balance, the single coin left off is the heavy one; if not, you have 3 coins.
- From 3 coins, weigh 1 against 1. The heavier side is the coin, or the one left off if they balance.
What they are checking: Using an information count to get a lower bound, then a construction that meets it. Strong candidates give both halves.
Q10. Erasing pairs of numbers (Brain Teaser, Medium)
The numbers 1 through 10 are written on a board. A move is: pick any two numbers on the board, erase them, and write their absolute difference. After nine moves one number is left. Can it be 0? What is the largest value it can be?
Answer: It can never be 0, and the largest possible value is 9. For any two numbers a and b, the values a + b and |a - b| are both even or both odd, so a move never changes whether the board's total is even or odd. The total starts at 55, which is odd, so the last number is odd and cannot be 0. A move never creates a number bigger than the largest one already on the board, so the last number is at most 10, and being odd it is at most 9. To reach 9, set the 9 aside and wipe out everything else: combine 10 with 8 to get 2, and 3 with 2, 5 with 4 and 7 with 6 to get three 1s. That leaves 9, a 2 and four 1s. Combine the 2 with a 1 to get 1, pair the four 1s into two 0s, combine the 0s, and finally combine 9 with 0 to finish on 9.
What they are checking: Looking for an invariant (a property no move can change) instead of trying cases, then backing up a bound with an explicit construction.
Quant mental math questions
Q11. Multiply 68 by 72 (Mental Math, Easy)
Compute 68 × 72 in your head.
Answer: 4,896. The two numbers sit 2 either side of 70, so the product is 70² - 2² = 4,900 - 4 = 4,896. The pattern (a - b)(a + b) = a² - b² turns many products into one easy square and a small correction.
What they are checking: Speed and number sense: spotting structure instead of doing long multiplication.
Q12. Doubling time at 7% (Mental Math, Easy)
Money grows steadily so that it is worth 7% more after each full year; after t years it is 1.07ᵗ times the starting amount. Roughly how many years does it take to double? A fractional answer is fine.
Answer: About 10.2 years. The rule of 72 gives 72 / 7, about 10.3, which is a good first guess. More precisely, the doubling time solves 1.07ᵗ = 2, so t = ln 2 / ln 1.07. ln 2 is about 0.693 and ln 1.07 is a bit under 0.07, about 0.0677, which gives about 10.24. As a check, 1.07¹⁰ is about 1.967, just short of 2, and 1.07¹¹ is about 2.105, so the answer sits a little above 10.
What they are checking: Comfort with compounding and logarithms, plus a fast approximation you can sanity-check.
Market-making and betting questions
Q13. Pricing a squared die (Market Making and Betting, Easy)
I roll one fair six-sided die and pay you the square of the result in dollars. What is the fair value of this game, and what two-sided market (a price you would buy at, the bid, and a price you would sell at, the ask) would you quote?
Answer: The fair value is 91/6, about $15.17. The squares 1, 4, 9, 16, 25 and 36 are equally likely and add to 91, so the average is 91/6. A sensible market brackets the fair value with a small edge on each side, for example 14.75 bid and 15.60 ask. The payout is very spread out: its standard deviation is about $12.2 per roll, so a few tens of cents of edge is small next to the risk on any single trade, which argues for trading modest size or quoting a little wider. Note that the fair value is not 3.5² = 12.25; the average of the squares is larger than the square of the average.
What they are checking: Computing an expectation quickly, centering a market on it, and thinking about risk instead of only the average.
Q14. Three dice and partial information (Market Making and Betting, Medium)
I roll three fair six-sided dice and you make a market on their total. (a) Where do you center your market with no information? (b) I look at the first die only and tell you it is a 6. (c) Instead, I look at all three and tell you only that at least one of them is a 6. Where do you center in each case?
Answer: (a) 10.5, (b) 13, (c) 1143/91, about 12.56.
- Each die averages 3.5, so three dice average 10.5.
- If the first die is a 6, the other two are unaffected and average 7 together, giving 13.
- For (c), split all outcomes into "at least one 6" and "no 6". With no 6, each die is uniform on 1 to 5 and averages 3, so that group averages 9, and it has probability (5/6)³ = 125/216. The overall average is still 10.5, so 10.5 = (91/216) × E + (125/216) × 9, which gives E = 1143/91, about 12.56.
Statement (c) moves you less than (b) because 75 of the 91 outcomes with a 6 contain exactly one 6, and in those the other two dice are known not to be 6s, so they average 3 each rather than 3.5.
What they are checking: Updating a fair value on new information, and noticing that two statements that sound alike carry different information.
Q15. Sizing a repeated edge (Market Making and Betting, Medium)
You can repeatedly bet any fraction of your current bankroll on an even-money bet that you win 55% of the time. What fraction should you bet each time to make your bankroll grow fastest in the long run? What happens if you bet 25% each time instead?
Answer: Bet 10% each time. At 25% your bankroll shrinks in the long run, even though every single bet has a positive expected value. Betting a fraction f multiplies your wealth by 1 + f on a win and 1 - f on a loss, so the long-run growth rate per bet is g(f) = 0.55 ln(1 + f) + 0.45 ln(1 - f). Setting its derivative to zero, 0.55/(1 + f) = 0.45/(1 - f), gives f = 0.55 - 0.45 = 0.10; this is the Kelly fraction, the bet size that maximizes long-run growth. Plugging in, g(0.10) is about +0.50% per bet, while g(0.25) is about -0.67% per bet. Overbetting hurts because losses compound harder than gains: after a 25% loss you need a 33% gain to get back to even.
What they are checking: Whether you separate expected value from compounding, and understand that bet size, not only edge, decides whether you grow.
Coding questions
Q16. Two days that sum to a target (Coding, Easy)
You are given a list of daily profit and loss (P&L) figures, as integers that may be negative, and a target value. Write a function that says whether two different days add up exactly to the target. Do better than checking every pair.
Answer: Make one pass with a hash set (a set with fast membership checks), so the work grows in line with the length of the list, O(n) time and O(n) memory.
- Start with an empty set of values seen so far.
- For each value x, check whether target - x is already in the set. If it is, return true.
- Otherwise add x to the set and move on. If the loop finishes, return false.
Checking before inserting stops a single day from being paired with itself, and it still handles repeated values correctly, for example two separate days of 50 with a target of 100. If memory is tight, sort the list instead, in place or on a copy if the original order must be kept, then walk two pointers inward from both ends. That takes O(n log n) time and no extra memory when you sort in place.
What they are checking: Whether you trade memory for speed, state the cost of your approach, and handle edge cases such as duplicates and self-pairing without being prompted.
Q17. A fair pick from a stream (Coding, Medium)
Trades arrive one at a time in a stream whose length you do not know in advance. Using a single pass and constant memory apart from a counter, choose one trade so that every trade in the stream is equally likely to be the one you end up with.
Answer: Store only the kept trade and a count of trades seen. Keep the first trade. When the k-th trade arrives, replace your kept trade with it with probability 1/k; otherwise keep what you have. To see that this is fair, take a stream of n trades and look at trade number i. It is picked when it arrives with probability 1/i, and it then survives each later arrival j with probability 1 - 1/j = (j - 1)/j. The product (1/i) × (i/(i+1)) × ... × ((n-1)/n) cancels down to 1/n, the same for every trade. This is called reservoir sampling, and it extends to keeping k trades: the m-th arrival (for m greater than k) enters with probability k/m and replaces a randomly chosen kept trade.
What they are checking: Designing under a memory limit, and proving that a randomized method is fair rather than just asserting it.
Q18. Maximum drawdown in one pass (Coding, Medium)
Given a list of positive prices in time order, compute the maximum drawdown: the largest percentage fall from a peak to a later low. Your solution should look at each price only once.
Answer: Track the highest price seen so far and the worst drawdown seen so far. At each price p, update the peak to the larger of the peak and p, compute (peak - p) / peak, and keep the largest such value. Return it at the end; this takes O(n) time and constant memory. It is correct because, for any later price p, the fall 1 - p / (earlier price) is largest when the earlier price is the highest one before it, which is exactly the running peak. A common mistake is to compare the overall maximum with the overall minimum, which ignores time order: a low that comes before the high is not a drawdown.
What they are checking: Turning an all-pairs comparison into a single pass by keeping the right running state, and respecting time order.
Behavioral questions
Q19. When your analysis was wrong (Behavioral)
"Tell me about a time an analysis or model of yours turned out to be wrong."
Answer: There is no single right answer, but strong answers cover the same ground. Pick a real, specific case with numbers, not a strength in disguise. Say what you believed and why it seemed reasonable at the time. Explain how the error came to light, ideally because you checked your own work. Be clear about what it cost, or could have cost. Spend the most time on what you changed afterward, such as a test, a check or a habit, and give evidence that you have used it since. Own the mistake without blaming a teammate or the data.
What they are checking: Self-awareness, ownership of mistakes, and whether you build safeguards after one. Trading and research teams rely on people who report their own errors early.
A preparation plan
My core advice is daily practice, started before your first assessment arrives.
Daily habits
- Mental math, 30 to 60 minutes a day, the block I recommend.
- Coding on LeetCode, plus a free student HackerRank account, since most firms build their assessments on HackerRank.
- Probability, statistics and brain teasers, a few problems a day, solved out loud.
- Markets. Firms often ask how you keep up with markets, so have a real answer.
An eight-week block
- Weeks 1 and 2: probability and expected value.
- Weeks 3 and 4: statistics and brain teasers, plus timed mental math sets.
- Weeks 5 and 6: coding patterns and, for trading roles, market-making and betting games. Take a timed practice test on HackerRank.
- Weeks 7 and 8: mock interviews out loud, behavioral stories, and research on each firm: why this firm, why this team, why you.
Then keep the daily habits running through recruiting season. In my webinar What You Need to Do NOW to Land a 2027 Summer Quant Internship, I stressed the out-loud part: "Oftentimes, I find that a lot of my clients feel really confident about their behavioral interviews, but they've never practiced their stories or their questions out loud, and that's super important to do with ... a friend, a family member or maybe your roommate."
Resources
- The Green Book, A Practical Guide to Quantitative Finance Interviews. In my webinar What you need to do now to land a 2027 internship, I said: "I think the top resource is going to be the Green Book. If you don't know what the Green Book is, then you very much should. This is a book that has so many questions for prepping you in different areas, from math to probability, statistics, brain teasers and some of the technical questions."
- Heard on the Street, a second book to work through the same way.
- LeetCode for daily coding, and HackerRank for the timed assessment format.
- Jane Street's Probability & Markets Guide, which its interviewing page says covers many of the core concepts in its interviews.
For the order to study in, read why most quant prep is backwards.
What firms say about their own interviews
These notes come only from each firm's own careers pages. Processes vary by role and region, so check the page for yours.
- Jane Street says it does not test quant trading candidates on finance or economics, and instead looks at what it is like to solve a problem with you. It values asking good questions over knowing every answer, and lets you interview in the programming language you know best.
- Optiver's interview tips for software engineers say: "Most interviews test what you know. At Optiver, we're interested in how you think." The page advises asking clarifying questions at the start, engaging with hints, structuring behavioral answers with the STAR method (situation, task, action, result) and practicing out loud. Its Europe recruitment FAQ says assessments can be completed only once every 8 months, with no resets or retakes in that window once you start, so do not open one before you are ready.
- IMC's US recruitment page lists an application, an assessment for some roles and a series of interviews. For interns and graduates it requires no specific industry experience, looking instead for interest in financial markets and a strong analytical or technical background.
Common mistakes
- Going in cold. Not preparing daily for technical interviews, and not preparing for behavioral ones, are common failures.
- Leaving the assessment to the deadline. My warning: "You haven't prepped for your technicals, [and you think] I can answer that assessment at any time. No, they give you a deadline, whether that's 3 days, 5 days, 7 days or 30 days. You need to be prepared. And don't do that technical on the deadline, because you might have technical difficulties. Maybe their site is down."
- Applying to 50 roles in a day. Each one can send back an assessment. I suggest 5 to 15 at a time.
- Solving in silence, so the interviewer cannot credit your reasoning.
- Memorizing answers instead of methods. Interviewers change the numbers.
- Defending a wrong path after the interviewer nudges you toward another.
Frequently asked questions
What questions are asked in a quant interview?
Expect probability, expected value, statistics, brain teasers, mental math and coding, plus market-making and betting questions for trading roles. Every role also gets behavioral questions about your resume, your projects and why you want this firm.
How do I prepare for a quant interview?
Practice daily: 30 to 60 minutes of mental math, coding on LeetCode, probability and brain teasers out loud, and behavioral answers rehearsed with another person. Start before your first online assessment arrives, since its deadline can be only days away.
What is on a quant online assessment?
I describe it as anything from mental math to probability, statistics and coding, usually on HackerRank. Firms want a high score, not only a pass, before they move you to the HR interview.
Is the Green Book enough?
I name the Green Book as the top resource for math, probability, statistics and brain-teaser prep. I pair it with daily coding on LeetCode, a HackerRank account for the assessment format, daily mental math and behavioral answers practiced out loud.
How hard are quant interviews?
They are demanding: a timed assessment where firms want a high score, several rounds of live problem solving, and behavioral rounds on top. Most of what they test improves with steady daily practice.
What does Jane Street ask in interviews?
Jane Street's interviewing page says it does not test quant trading candidates on finance or economics and focuses on solving problems together. It links a Probability & Markets Guide that it says covers many core concepts from its interviews.
Get a second opinion
If you want a second opinion on your interview prep, you can request a free Discovery Session, a Zoom session with one of our coaches: quantminds.com/discovery-session-request.