Senior Quantitative Researcher

A senior quant’s job: find patterns the market hasn’t priced in.

You decide what is worth researching, validate it on a simulation that replays the market microsecond by microsecond, and take it into production, where it trades real size and you answer for how it behaves. Most ideas die in the simulation, because the market is unforgiving and the theory usually breaks on the first assumption that does not hold. The edges that survive are faint and they decay, so the work is as much judgment as computation: knowing which signal is real, how hard to push it, and when it has stopped paying.

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Position

Senior Quantitative Researcher

Employment

Full-time

Work Model

On-site

Location

Spork Palace, Prague 1

Team

Quantitative Researcher

Interested in working for Qminers?

Senior Quantitative Researcher

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• Own a strategy end to end, from research to live trading.
• Generate your own research ideas. We also keep a backlog of our own, from improvements to existing strategies to entirely new ones. Depending on your experience, you may drive the research direction yourself.
• Build models that predict where price is going, then design how to execute the signal, and watch what the resulting algorithm actually does in the market.
• Work in small teams, in the open, with no pods. Feedback on your ideas is immediate.

Most of our day is thinking, arguing toward the best solution, and coding. We like results reasonably fast, in both development and runtime, and we give people real room to run and a direct line to the result.

Qminers is a Prague-based algorithmic trading firm competing on four continents, 24/7. Founded in 2012, we delivered a Sharpe ratio above 10 year after year for a decade. We are a team of around 50: mathematicians, quants, software engineers, and machine learning researchers.

Excellence is not a slogan here, it is the baseline.

Must have

• 3+ years of professional experience in quantitative research or systematic trading, or in a comparably rigorous, model-driven field.
• A track record of taking your own work into production and owning what happens once it's live — not only prototyping or publishing.
• Strong command of mathematical modeling and statistical methods, and the ability to reason through problems that have no textbook answer.
• The ability to explain your reasoning clearly enough that a colleague can challenge it.
• Fluent Python. Most of the code is agent-written; the judgment about whether it is right is not, and the result is yours either way.
• Practical experience with machine learning methods, and a clear sense of when they help and when a simpler model is the honest answer.

Nice to have

• A research background: peer-reviewed publications, conference work, or a PhD. Many of us have one.
• Experience building agentic tooling for your own research or engineering workflow — automating parts of the research loop, not just using a chat assistant.
• Experience in high-frequency or low-latency trading, where microstructure and execution matter as much as the signal.
• Deep expertise in an area we lean on: optimal execution, stochastic modeling, time series, or ML applied to markets.

We are honest about fit. This is not a demo and it is not a benchmark: the data is genuinely hard, the signal is faint, and a result only counts when it holds up on real metrics. If you want to be only a researcher who hands off, or only an engineer who ships someone else’s model, this is the wrong seat. If you want to be both, it is the right one.

The best argument wins.

Meritocracy is in our DNA.

You own the outcome.

Responsibility here is total, the wins and the mistakes alike.

Your rivals are outside, not inside.

Excellent colleagues are the elevator up, not the competition.

Interview process

01

Application

Through the web form.

02

Screening call

A short conversation to align expectations. You know where you stand.

03

Technical screening

An online test and a short technical call.

04

Take-home task

A real problem, a reasonable amount of time, and detailed feedback either way.

05

On-site interview

One-to-one conversations with the team, and lunch together.

06

Decision within a week

A clear answer, and if it did not work out, what was missing so you can try again.

Base salary

Every role has a published salary range, and the more experience and results you bring, the higher you move within it.

Profit share & bonuses

Everyone holds a share of net profit, up to low single-digit percentages depending on impact. Around a quarter of the firm’s total annual profit goes into bonuses, re-evaluated every year on real, measurable impact.

Shadow equity

A meaningful share in the firm’s performance, a standard part of compensation for senior contributors.

ESOP

For the most senior people, genuine ownership in what the firm becomes. The details are worth a conversation.

We publish our annual results, and anyone inside the firm can see how Qminers is paid and how the current year is shaping up. Financial transparency is a given, not a perk.

Interested in working for Qminers?

Send an email to hr@qminers.com or fill out the form.

Are you an experienced quant? A programmer with a passion for C++ or Python? Or are you a recent graduate with a specialization in data? We’re always keen to hear from you!

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