Tower Research Capital Quantitative Analyst interview typically runs 2 rounds: a 2-hour onsite coding assessment and a follow-up interview. The process usually takes about 1-2 weeks and is notably resume-deep and conversational.
$166K
Avg. Base Comp
$226K
Avg. Total Comp
2-3
Typical Rounds
1-3 weeks
Process Length
Our candidates report that Tower Research Capital cares less about polished scripting and more about whether you can defend the work on your resume line by line. In one accepted-offer experience, the interviewer spent most of the conversation probing a recent internship, turning each project into a series of follow-ups. That pattern suggests they’re looking for real ownership and technical clarity, not just familiarity with buzzwords. If you say you built, optimized, or analyzed something, expect to explain the assumptions, tradeoffs, and edge cases behind it.
A recurring theme is that Tower mixes that depth check with a light but telling layer of probability intuition. The die-roll expected value question is a good example: the candidate didn’t finish it perfectly, yet still advanced, which tells us the bar is not flawless recall. What seems to matter more is whether you can reason cleanly under pressure and stay coherent when the problem is unfamiliar. We’ve seen this kind of process reward candidates who can move from intuition to structure without getting rattled.
The other non-obvious signal is the tone: the experience was described as detailed but fair, with time left for questions and a conversational feel. That matters because it implies Tower is evaluating how you think in a real working exchange, not just how fast you can produce an answer. Our read is that depth on your recent experience and sound probabilistic reasoning are the two levers that most often separate strong candidates from everyone else.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the Tower Research Capital process.
Most of my process ran over video, and the thing I'd want someone prepping to know is that the interviewers changed a lot. I had several virtual technical rounds, each with a different member of the team, so the questions and the style shifted from one meeting to the next. Some of the early conversations were built around my resume. They asked about specific projects I'd listed and followed up on the details, so anything you put on your resume should be something you can explain in depth.
The technical content covered probability, statistics, machine learning, and market making. Probability and statistics came up across several rounds, and the market making questions were framed around trading problems, not just pure math, so it helped to think about how the math connects to making decisions under uncertainty. I was never sure which track I was really being evaluated for, since there was no clear separation between research, trading, and dev, so I'd expect to be tested broadly regardless of the title on the posting.
After the virtual rounds I went to the office for an onsite made up of several rounds that felt similar in content to the virtual ones, just in person. It was a long day of back-to-back technical conversations with different people. I eventually received an offer and ended up declining it. Overall, the interviewers were engaged and the questions were fair, but the process was demanding and the expectations across roles were not always clearly defined.
Prep tip from this candidate
Be ready to defend every project on your resume in depth, since interviewers followed up on specifics in the virtual rounds. Drill probability and statistics alongside market making scenarios, because those were the recurring technical themes.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Tower Research Capital
Select the 2nd highest salary in the engineering department
| Question | |
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| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| 500 Cards | |
| Maximum Profit | |
| Employee Salaries | |
| Merge Sorted Lists | |
| Bagging vs Boosting | |
| Subscription Overlap | |
| Comments Histogram | |
| Top 5 Turnover Risk | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report | |
| Google Maps Improvement | |
| Cumulative Distribution | |
| Slacking Employees Salaries | |
| Hurdles In Data Projects | |
| Sum to N | |
| New Partner Card | |
| Variable Error | |
| Jars and Coins | |
| Real-Time Transaction Streaming | |
| Compute Deviation | |
| Find the Missing Number | |
| String Shift | |
| Compute Variance | |
| Prime to N | |
| Session Difference | |
| Department Expenses |
Synthesized from candidate reports. Individual experiences may vary.
The process appears to start with an initial conversation to assess fit and basic background. Based on the experience, this stage likely focuses on resume review and whether the candidate has the quantitative and technical foundation needed for the role.
Candidates complete a two-hour onsite coding assessment. This stage is technical and time-pressured, serving as an early filter before the team spends more time on the candidate’s background and reasoning.
After the assessment, candidates meet with a team member for a follow-up technical interview. The interviewer digs deeply into recent internship or project work, asking detailed follow-up questions about the technical decisions, implementation, and results.
A probability question is included in the conversation, such as finding the expected number of rolls until the first six appears on a fair die. The emphasis is on reasoning and intuition under pressure rather than memorizing formulas or getting every answer perfect immediately.
The interviewer leaves time for the candidate to ask questions, making the discussion feel conversational and fair. This suggests the team values two-way engagement in addition to technical depth.
