Quantitative Researcher - Tick-Level Orderbook Signals

Tradermath; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

DV Trading is seeking a Quantitative Researcher focused on orderbook-driven signal generation. You will work with senior researchers and traders to develop, test, and refine signals and models that inform trading decisions, with hands-on work on limit orderbook data.

You will build and backtest quantitative models, develop data pipelines for high-frequency market data, and apply statistical and machine learning techniques to improve signal quality while monitoring live performance.

Qualifications

• Experience with orderbook/LOB data.
• Degree in quantitative field.
• Strong Python with data science libraries (pandas, NumPy).
• Genuine interest in financial markets and market microstructure.
• Solid foundation in statistics.
• Strong problem-solving and intellectual curiosity.
• Ability to communicate technical findings to non-technical stakeholders.

Responsibilities

• Analyze high-frequency limit orderbook data to identify patterns and predictive signals.
• Build and backtest quantitative models using historical tick and orderbook data.
• Collaborate with senior researchers and traders to translate findings into production-ready strategies.
• Develop and maintain data pipelines for processing large-scale market data.
• Apply statistical and machine learning techniques to improve signal quality.
• Continuously monitor and iterate on live signals and models based on performance.

Skills

Orderbook data

Python

Statistics

Machine learning

Backtesting

Data pipelines

Education

Numerical degree

Tools

pandas

NumPy

Job description

DV Trading is seeking a Quantitative Researcher focused on orderbook-driven signal generation. You will work with senior researchers and traders to develop, test, and refine signals and models that inform trading decisions, with hands-on work on limit orderbook data.

You will build and backtest quantitative models, develop data pipelines for high-frequency market data, and apply statistical and machine learning techniques to improve signal quality while monitoring live performance.