ML Quant Researcher

Role Summary - ML Quantitative Researcher

This role sits at the intersection of quantitative research and engineering within a systematic trading environment. As a Machine Learning Quantitative Researcher, you will be responsible for developing, evaluating, and deploying trading signals using advanced statistical and machine learning techniques. The position requires a strong balance between theoretical rigour and practical implementation, with a focus on transforming research ideas into production-ready systems that directly impact trading performance.

Core Responsibilities

1. Research & Signal Development
You will conduct research into predictive signals using large-scale financial datasets, applying sophisticated machine learning methods to extract alpha. This includes working beyond standard linear models and basic ensembles, exploring modern statistical learning approaches to improve signal robustness and performance.

2. Machine Learning Application
A key component of the role is utilising ML techniques for feature selection and signal combination in high-dimensional datasets. You will analyse complex relationships within financial time series and design models capable of capturing non-linear interactions while maintaining interpretability and robustness.

3. Model Validation & Risk Control
You will design and implement rigorous validation frameworks to evaluate trading signals, including out-of-sample testing and cross-validation techniques tailored to time-series data. A critical part of the role is identifying and mitigating risks such as overfitting, data leakage, and structural instability in models.

4. Critical Evaluation of Signals
Working in a highly analytical environment, you will assess proposed signals and models with a sceptical, hypothesis-driven mindset. This includes stress testing ideas, identifying failure modes, and ensuring statistical validity before deployment into production systems.

5. Productionisation of Research
You will collaborate closely with other researchers and engineers to transition models from research to live trading. This involves writing production-quality code, optimising performance, and ensuring reliability and scalability of deployed models.

6. Data & Pipeline Engineering
The role requires building and maintaining scalable data pipelines and machine learning workflows. You will work with large datasets, ensuring efficient data ingestion, processing, and integration into both research and production environments.

Key Skills & Profile

You are expected to bring strong quantitative and programming expertise, combined with a practical mindset:

• Deep understanding of statistical learning, probability, and modelling techniques
• Proficiency in Python and experience developing production-quality systems
• Experience handling large datasets, including time-series and structured financial data
• Strong analytical thinking with a sceptical, research-driven approach to problem solving
• Ability to translate theoretical models into robust, scalable implementations
• Familiarity with systematic trading concepts, alpha generation, and portfolio construction is advantageous

What Success Looks Like

Success in this role means consistently delivering robust, well-validated signals that can be deployed into live trading environments. You will contribute to improving the research pipeline, enhancing model reliability, and ensuring that machine learning methods are applied in a disciplined and statistically sound manner. Over time, your impact will be measured by the quality, scalability, and performance contribution of the models you help bring into production.

Overall Positioning

This is a high-impact role suited to individuals who enjoy operating across the full lifecycle of quantitative research, from ideation and experimentation to deployment and optimisation. It offers the opportunity to work on complex, real-world problems where strong mathematical intuition, engineering capability, and scientific rigour are equally essential.