We are a prop-trading firm that blends the agility of a startup with the capabilities of a high-performing fund.
We build advanced, data-driven trading strategies across asset classes, and foster a culture where ideas matter, ownership is encouraged, and every team member can unlock their full potential.
This role is for a Senior quant who wants to own the full lifecycle of a strategy: generate ideas, research and validate them properly, deploy to production, and run the strategy with robust risk control.
We can provide a strategy direction / starting thesis and the data/infra — you take it to a live, trading system.
We care about good decisions and robust strategies, not about specific models or fashionable architectures.
ML is a tool, not a goal.
What you will do  Formulate and test investment hypotheses; turn them into live systematic strategies.
 Build the full pipeline: data, signals/features, model, execution, risk, monitoring.
 Design validation for markets: non-IID data, regime shifts, leakage control, walk-forward.
 Own risk: position sizing, limits, stress tests, scenario analysis, tail-risk awareness.
 Model real trading constraints: transaction costs, slippage, market impact, and capacity.
 Run post-trade analytics: PnL attribution, drawdown analysis, signal decay, and model drift.
 Partner with research/engineering/execution to make sure strategies actually trade.
What success looks like  You can take a thesis from “idea” to a live strategy with solid validation and risk controls.
 You can explain what drives PnL, when it breaks, and how you detect/mitigate drift.
 You raise the bar for research rigor and production readiness.
Culture of innovation  — a genuinely open, research-driven environment where curiosity is rewarded and your ideas directly shape real trading strategies.
True flexibility  — work from anywhere; we care about outcomes, not where or when you sit at your desk.
High autonomy & ownership  — no micromanagement, no bureaucracy.
You get full responsibility over your research direction, models, and production impact.
Startup agility, Fund resources  — fast decision-making, minimal red tape, and access to the data, compute, and infrastructure you need to run serious research.
Massive data advantage  — work with a uniquely rich multi-modal dataset (order log, options chains, satellite data, alt-data, Bloomberg, proprietary feeds).
Top-tier equipment  — choose the hardware/software setup that makes you most productive.
Well-being support  — 35 days of vacation, 100% paid sick leave, and access to a corporate psychologist.
Real career growth  — shape research culture, lead initiatives, and influence long-term strategy directions.
 5+ years in quantitative research, systematic trading, or ML-driven modeling for markets.
 Proven production deployment of strategies/models with measurable outcomes (live trading or equivalent).
 Strong stats + time-series fundamentals (non-stationarity, dependence, tails, robustness).
 Strong Python and engineering discipline (reproducible research, clean code, tests/monitoring mindset).
 PM-style thinking: returns vs risk vs costs, not “models for models”.
English and Russian are both working languages, and we are particularly open to Russian-speaking quants with international backgrounds.
We work in an international, multilingual team.
Nice to have Experience trading options/volatility, strong ML for time series, or a deep applied math/physics/econophysics background is a major plus.
Tech (what you’ll use)  Python; PyTorch if ML-heavy  Docker; experiment/research reproducibility tools (DVC/MLflow—tooling not strict)  Large, multi-source datasets (market + macro + alternative data) You are not expected to be an expert in every area.
We value ownership, depth, and good judgment over trying to cover everything at once.