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.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Tower Research Capital process.
The hardest part for me was how much they dug into my last internship. The interviewer spent a lot of time asking follow-up questions about the technical work I had done there, so it felt less like a scripted screen and more like a deep dive into my actual experience. The interviewer was very nice and also left time for me to ask questions, which made the conversation feel pretty fair even though it was detailed.
There was also one probability question in the mix: rolling a fair six-sided die and finding the expected number of rolls until the first 6 appears. I didn’t solve it cleanly on the spot, but I still moved forward to another interview afterward, so they seemed to care more about how I reasoned than whether I got everything perfect immediately. Separately, the broader process I went through for the quant trading intern role included a 2-hour onsite coding assessment followed by a follow-up interview with a team member, where the emphasis was on resume depth and probability/statistics intuition rather than memorized formulas. Overall it felt technical but conversational. My main takeaway is to be ready to explain every line of your recent internship/project work in detail and to practice basic expected value and probability questions under pressure.
Prep tip from this candidate
Be ready to defend your most recent internship/project work in detail, since they asked a lot of follow-up questions there. Also practice simple expected-value problems like the first-success die roll question, because that style of probability came up directly.
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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 | |
|---|---|
| Empty Neighborhoods | |
| Merge Sorted Lists | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Bagging vs Boosting | |
| Employee Salaries | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Subscription Overlap | |
| Slacking Employees Salaries | |
| Find the Missing Number | |
| Cumulative Distribution | |
| Compute Deviation | |
| Maximum Profit | |
| Prime to N | |
| String Shift | |
| Last Transaction | |
| Department Expenses | |
| 500 Cards | |
| Session Difference | |
| Random SQL Sample | |
| Rain in N Days | |
| Assumptions of Linear Regression | |
| Alphabet Sum | |
| Paired Products | |
| Bank Fraud Model | |
| Rectangle Overlap | |
| Hurdles In Data Projects | |
| Unique Work Days |
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.