Quantitative Financial Specialist
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, pandas, SciPy, statsmodels) specifically for research, backtesting, and trading system development, not general software engineering
Experience with time-series modelling, factor 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 thesis work in quantitative finance, econometrics, or a related empirical 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 flow)
Portfolio optimisation under real-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/LSEG, or equivalent
Cloud platforms (AWS / GCP / Azure) and workflow orchestration (Airflow, Prefect) is a plus
Apply To This Job
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, pandas, SciPy, statsmodels) specifically for research, backtesting, and trading system development, not general software engineering
Experience with time-series modelling, factor 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 thesis work in quantitative finance, econometrics, or a related empirical 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 flow)
Portfolio optimisation under real-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/LSEG, or equivalent
Cloud platforms (AWS / GCP / Azure) and workflow orchestration (Airflow, Prefect) is a plus
Apply To This Job