Senior Quant Researcher (Prop / Systematic trading) at GLP Transit

Barcelona, Catalonia, Spain -

Full Time

Start Date

Immediate

Expiry Date

07 May, 26

Salary

0.0

Posted On

06 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Quantitative Research, Systematic Trading, Machine Learning, Python, Statistical Analysis, Risk Management, Data Analysis, Model Validation, Post-Trade Analytics, Time-Series Analysis, Position Sizing, Stress Testing, Scenario Analysis, Market Impact, Transaction Costs, Slippage

Industry

Description

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. 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. 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.

Responsibilities

Formulate and test investment hypotheses, turning them into live systematic strategies while managing the full pipeline from data to execution. Own risk management aspects including position sizing, limits, and stress tests.