We are a research-focused prop trading company building systematic trading strategies across multiple asset classes.

We’re hiring a Senior Quant who can own a strategy end-to-end: 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.

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, 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.

Must-have

• 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”.

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)

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.

What we offer

• High autonomy and real ownership of research direction and production impact

• Remote flexibility; outcome-focused culture

• Serious data/compute/infra to run research properly

• 35 vacation days, fully paid sick leave, well-being support