Role OverviewWe 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 ResponsibilitiesResearch, develop, and validate systematic trading strategies — including statistical arbitrage, momentum, mean reversion, and factor modelsWrite clean Python code to implement backtesting frameworks, signal generation pipelines, and execution logic with proper out-of-sample validation and transaction cost modellingDevelop 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 contextPerform risk analysis including factor exposure decomposition, drawdown analysis, and stress testing across market regimesDocument research methodology, model assumptions, and backtest results to rigorous engineering and research standardsRequired QualificationsMaster's or PhD in a quantitative discipline: Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or similar2–5 years of hands-on experience in quantitative research, systematic trading, or a closely related role at a hedge fund, prop shop, or asset managerSolid understanding of financial markets, trading mechanics, and market microstructure. You should be comfortable interpreting a P&L attribution and spotting a flawed backtestProficiency in Python (NumPy, pandas, SciPy, statsmodels) specifically for research, backtesting, and trading system development, not general software engineeringExperience with time-series modelling, factor analysis, and statistical inference applied to financial dataFamiliarity with execution concepts and market data infrastructure (order types, slippage, tick data, market impact)Ability to build financially-grounded quantitative models rather than purely data-driven black boxesPreferred QualificationsPublished research or thesis work in quantitative finance, econometrics, or a related empirical fieldBackground in high-frequency trading, market making, or latency-sensitive executionFamiliarity with machine learning applied to finance (gradient boosting, sequence models, reinforcement learning for execution)Exposure to one or more of the following:Options pricing, volatility modelling, or derivatives tradingAlternative data sourcing and signal extraction (NLP, satellite, order flow)Portfolio optimisation under real-world constraints (transaction costs, turnover limits, risk budgets)Crypto markets, DeFi protocols, or digital asset microstructureTech Stack / ToolsPython (NumPy, pandas, SciPy, scikit-learn, statsmodels)SQL and version control (Git)Market data APIs: Bloomberg, Refinitiv/LSEG, or equivalentCloud platforms (AWS / GCP / Azure) and workflow orchestration (Airflow, Prefect) is a plus
