How many interview rounds does Flow have for a Quantitative Researcher?

Flow’s loop typically starts with a recruiter screen, followed by online technical assessments. If you progress, you then do technical rounds with Quantitative Researchers, and there can be a final technical evaluation with senior leadership such as the head of trading. Across 14 reported interviews, the most common self-reported difficulty is average, which suggests a broadly steady but competitive process.

What do the Flow Quantitative Researcher interview assessments test?

Flow’s online technical assessments cover statistics, machine learning, and Python coding. In technical rounds, you can expect deep dives into past research projects or strategy implementation with current Quantitative Researchers. The final technical evaluation may include discussions with senior leadership, such as the head of trading.

What topics should I prioritize for Flow Quantitative Researcher interviews?

Prioritize high-frequency trading strategy development and financial markets microstructure, since these are explicitly listed top topics. The preparation is also aligned with regression analysis and core probability fundamentals, plus systematic trading track record and execution modeling or trading execution quality. Equities trading (US cash equities) and pricing models for execution are also highlighted topics to study.

What coding skills does Flow expect from Quantitative Researcher candidates?

You should be ready to code in Python, since Python coding is part of the online technical assessments. Flow also expects practical performance considerations for high-frequency data processing in Python, along with the ability to clean and feature-engineer a dataset for a predictive model. The guide also emphasizes structuring data-science pipelines for large-scale datasets.

What is the pay range for a Flow Quantitative Researcher?

Candidate and job-posting reports point to $200k to $225k total compensation at Flow, with base pay starting at $200k. Total pay varies by level and location, so expect the upper end to depend on those factors.

How hard is it to get an offer at Flow as a Quantitative Researcher?

In 14 reported interviews, candidates most often rated the difficulty as average. The offer rate reported is 7%, so the process is competitive even when the difficulty level feels manageable for many candidates.

FlowQuantitative Researcher

Updated Apr 27, 2026

Flow Quantitative Researcher Interview Experiences 2026

Real, anonymous reports from people who interviewed for Quantitative Researcher at Flow, newest first and distilled into what to expect across the loop.

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Hot & recentNewest first

6 months ago

Average Neutral New York, NY

After the recruiter reached out, I moved pretty quickly into online assessments and then two back-to-back technical rounds, each about an hour. The technical questions felt like they were testing an academic, methodical way of thinking rather than anything intuitive. I remember the questions shifting in focus between the two rounds, but mentally it was the same theme: quick quantitative reasoning, then more applied thinking around trading concepts.

Not long after, I was pulled into an onsite style loop that included more mental math tied to financial products and ETFs. The trader-side conversation wasn’t just me giving answers—it turned into a discussion where I had to talk through what I’d chosen, handle follow-ups, and then deal with more mental-math prompts while I was trying to be sure my reasoning was actually landing. The overall pace felt controlled enough that it never turned into a panic, even though it was clearly a “get it right quickly” format.

> 1 year

Difficult Positive New York, NY

My first step was an online mental-math test with strict timing—short window, lots of questions, and multiple levels of difficulty. After that, the process moved into interviews that felt more like they wanted to see how I handled motivation and then how quickly I could switch into trader-style technical thinking.

I didn’t make it very far, because the later screens that came with it were heavy in technical breadth. One stage was an extended assessment focused on machine learning, statistics, and data science topics, which felt average to difficult and covered a lot of ground. Another path I tried involved a HackerRank-style technical test with probability, stats, ML algorithms, and regression/data analysis—again, clearly focused on technical coverage rather than just core arithmetic.

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