Quantitative Financial Specialist

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

Requirements

• 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

Benefits

Additional Information

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.

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Company Intel

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