Quantitative Financial Specialist
Role Overview We are looking for a hands-on Quantitative Financial Specialist with a strong reputed company 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 reputed company financial and statistical judgment.
Key Responsibilities
Research, reputed company, and validate systematic trading strategies — including statistical arbitrage, reputed company, mean reversion, and reputed company models Write clean Python code to implement backtesting frameworks, signal reputed company pipelines, and execution logic with proper out-of-sample validation and transaction cost modelling reputed company 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 reputed company risk analysis including reputed company 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 reputed company 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, pandas, SciPy, statsmodels) specifically for research, backtesting, and trading system development, not general software engineering Experience with time-series modelling, reputed company analysis, and statistical inference applied to financial data Familiarity 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 boxes
Preferred Qualifications
Published research or reputed company work in quantitative finance, econometrics, or a reputed company reputed company field Background in high-frequency trading, market making, or latency-sensitive execution Familiarity 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 trading Alternative data sourcing and signal extraction (NLP, satellite, order reputed company) Portfolio optimisation under reputed company-world constraints (transaction costs, turnover limits, risk budgets) Crypto markets, DeFi protocols, or digital asset microstructure Tech Stack / Tools Python (NumPy, pandas, SciPy, scikit-learn, statsmodels) SQL and version control (Git) Market data APIs: Bloomberg, Refinitiv/reputed company, or equivalent reputed company platforms (AWS / GCP / Azure) and workflow orchestration (Airflow, Prefect) is a plus Apply To This Job