ML Quantitative Researcher

6 000 – 8 000 $

≈ 519,5 тыс.–692,7 тыс. ₽

🌍 УдалённоMiddleПолная занятостьАнглийский B2

6 000 – 8 000 $≈ 519,5 тыс.–692,7 тыс. ₽

Сильная вакансия

Платят на 79% выше медианы грейда, навык востребован (Python).

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Описание вакансии

We're looking for people with a strong ML background to work across Quant Research and Feature Engineering. No finance experience required - we'll teach you everything, from market microstructure to portfolio construction. We care about how you think and your ability to ask the right questions of data.

What you'll be doing

Formulate and test hypotheses about market inefficiencies - and stay honest when the backtest says no.

Think ahead about how model predictions will be integrated into trading - directional bets, spread convergence, or something else.

Design features with genuine predictive power, from classical statistical transformations to learned representations. Every feature is a hypothesis about the market encoded in a number, and you own the quality of that input.

Work with alternative data, time series, and nonlinear dependencies to find signal in places others overlook.

Build data pipelines that work reliably in production, not just in a notebook.

What we're looking for

Deep ML understanding, not just knowing the APIs, but having a clear sense of why gradient boosting tends to outperform transformers on tabular data, when a Bayesian approach beats a frequentist one, and what information leakage looks like in a time series context.

Proven hands-on experience with both deep learning and gradient boosting frameworks – a strong conceptual understanding of neural network internals is essential.

Serious attention to data leakage. In finance, the future bleeds into the past in non-obvious ways, and you need to spot these issues before they invalidate a backtest.

Solid math background: statistics, probability theory, stochastic processes.

Solid Python skills. Experience with pandas, Polars, or streaming data processing is a plus.

A statistical mindset: you don't just apply methods, you understand their assumptions, limitations, and failure modes.

Professional English proficiency at B2-level or above.

Nice to have

Graduate of SHAD or a similar top-tier technical or quantitative program.

Background in competitive mathematics, physics, or computer science olympiads.

Demonstrated performance in Kaggle or other ML competitions.