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