QuantitativeFinancialSpecialist

Role Overview We are looking for a hands-on Quantitative Financial Specialist with a strong foundation in systematic trading and quantitative research who can also build and ship production-grade code. This is not a data science role, you will be expected to deeply understand the markets you are modeling, the strategies you are deploying, and the risk you are managing. Python here is a means to an end: implementing models, running backtests, and building trading systems grounded in real financial and statistical judgment. Key Responsibilities - Research, develop, and validate systematic trading strategies — including statistical arbitrage, momentum, mean reversion, and factor models - Write clean Python code to implement backtesting frameworks, signal generation pipelines, and execution logic with proper out-of-sample validation and transaction cost modelling - Develop quantitative trading tasks grounded in market microstructure and financial theory (e.g. alpha decay analysis, regime detection, portfolio construction under realistic constraints) - Work directly with trading infrastructure, execution systems, and risk tooling to debug and validate strategy behaviour at the portfolio level in a simulated context - Perform risk analysis including factor exposure decomposition, drawdown analysis, and stress testing across market regimes - Document research methodology, model assumptions, and backtest results to rigorous engineering and research standards Required Qualifications - Master's or PhD in a quantitative discipline: Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or similar - 2–5 years of hands-on experience in quantitative research, systematic trading, or a closely related role at a hedge fund, prop shop, or asset manager - Solid understanding of financial markets, trading mechanics, and market microstructure. You should be comfortable interpreting a P&L attribution and spotting a flawed backtest - Proficiency in Python (NumPy, panda