Quantitative Researcher
QST Financial is a proprietary high-frequency trading firm, trading its own capital — no clients, no outside investors — since 2008. We work on top trading venues worldwide, finding and using market inefficiencies with statistical models, machine learning and our own technology.
Who we're looking for
Active, self-driven, sharp people with strong mathematical training and solid machine-learning skills. We work mainly in traditional financial markets but are happy to talk to strong candidates from crypto. Autonomy is high, the impact on results is direct, and compensation is tied to performance.
We consider candidates at any level, from strong graduates to experienced researchers. What matters is the strength of the candidate, not seniority or job title.
What you'll do
- Generate ideas, test hypotheses, and find new sources of alpha.
- Build, improve and maintain models based on statistics and machine learning.
- Work with large volumes of market data.
- Own the full research pipeline — from idea and experiments to validation and live trading — alongside the engineers.
What we expect
- Strong foundations in probability theory, statistics and algorithms.
- Confident Python and hands-on machine learning, including work with large datasets.
- The ability to run research rigorously and judge results critically: forming hypotheses, designing clean experiments, spotting overfitting, building robust validation, and dropping an idea when the data doesn't support it.
- Experience running research on real problems, from hypothesis to deployment.
- High standards, honesty with yourself about results, and comfort in a non-hierarchical team where ideas are challenged directly.
Nice to have
- Knowledge of C++ and low-latency execution.
- Fluency with modern AI tools to test hypotheses and build MVPs quickly.
We give preference to candidates who
One of these is a strong signal; several, stronger still.
- Are medalists of international olympiads in mathematics, informatics, competitive programming or physics.
- Hold a PhD or have a strong academic background in a relevant field, backed by publications, open-source projects or notable research.
- Have experience in algorithmic trading in general and HFT in particular, and can take an idea all the way to production.
- Show standout results in competitive data science and programming (Kaggle, Codeforces, ICPC).
How we work
The full research-to-production cycle happens in-house — our own trading platform, compute cluster and a dedicated data team, so researchers don't spend their time on infrastructure. A flat structure, and a short, workable path from hypothesis to launch without endless sign-offs.
Send your CV — and anything else you'd like us to see.
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