About the role:
We're looking for a Quantitative Analyst to help our financial services client drive best execution across FX and commodities. You'll build and validate models for transaction cost analysis (TCA), market impact, and algorithm performance — working at the intersection of market microstructure, statistics, and software engineering.
Key Responsibilities:
- Develop and maintain quantitative models for transaction cost analysis (TCA), market impact, slippage, and venue/algorithm performance.
- Conduct rigorous statistical analysis on tick, order, and execution data to identify sources of cost, latency, and information leakage.
- Build and maintain simulation and backtesting frameworks to evaluate execution algorithms, smart order routing logic, and venue selection under varied market conditions.
- Partner with traders, execution engineers, and brokers to translate research into improvements in live trading and order routing systems.
- Produce regular best execution reporting for internal stakeholders and investigate outliers or deteriorations in execution quality.
- Research advances in execution science — optimal trading, market impact modelling, and machine learning applied to microstructure — and assess their practical application.
- Document research findings clearly and present results to both technical and non-technical stakeholders.
- Maintain rigorous standards of model validation, code quality, and documentation across all execution analytics work.
Required Qualifications:
- Master's or PhD in a quantitative discipline such as Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or Econometrics.
- 3+ years of relevant work experience in a quantitative role covering FX or commodities (or both), ideally with exposure to execution, TCA, market microstructure, or algorithmic trading.
- Strong proficiency in Python; familiarity with libraries such as NumPy, pandas, scikit-learn, and statsmodels.
- Solid grounding in probability theory, statistics, linear algebra, and stochastic calculus.
- Experience working with large datasets and writing clean, performant, well-tested code.
- Demonstrated ability to translate research into production systems.
- Excellent written and verbal communication skills.
Preferred Qualifications:
- Hands-on experience applying machine learning techniques to high-frequency, order book, or execution data.
- Deep knowledge of market microstructure, execution algorithms, and smart order routing across lit, dark, and auction venues.
- Exposure to cloud platforms (AWS, GCP, or Azure) and modern data infrastructure (SQL, Parquet, kdb+).
Skills & Attributes:
- Analytical rigour: structured thinker who can decompose ambiguous problems into testable hypotheses.
- Intellectual curiosity: eager to explore new methods and challenge prevailing assumptions.
- Attention to detail: careful with data quality, model assumptions, and numerical precision.
- Pragmatism: balances theoretical elegance against real-world constraints and deadlines.
- Collaborative: thrives in a small, cross-functional team and welcomes constructive challenge.
