A leading quantitative investment firm is looking to hire a Junior Quantitative Researcher / Developer to join its Systematic Equities team. This role offers the opportunity to work on the research and development of alpha signals that feed directly into live investment strategies, combining quantitative research, data analysis and software engineering.

You'll work alongside experienced researchers to develop, test and improve predictive signals while building scalable research tools and infrastructure.

Responsibilities

1. Research, develop and evaluate new alpha signals across a range of equity datasets and investment universes.
2. Transform raw market, fundamental and alternative datasets into robust predictive features.
3. Build and maintain reusable, well-tested Python components for signal generation and research.
4. Analyse the statistical and economic significance of research findings using rigorous validation techniques.
5. Combine individual signals into robust composite models for systematic equity strategies.
6. Develop tools to automate research workflows, including experimentation and model evaluation.
7. Collaborate closely with the wider research and engineering teams to support the investment process.

Requirements

1. Master's or PhD in Mathematics, Physics, Statistics, Computer Science, Financial Engineering or another quantitative discipline.
2. Strong Python programming skills with experience writing clean, maintainable code.
3. Solid understanding of statistics, data analysis and quantitative modelling.
4. Experience working with pandas or Polars and collaborative development using Git.
5. Genuine interest in systematic investing and quantitative research.
6. Strong communication skills and a collaborative mindset.

Desirable Experience

1. Exposure to systematic or fundamental equity research.
2. Experience developing and evaluating quantitative alpha signals.
3. Familiarity with machine learning libraries such as scikit-learn, LightGBM or PyTorch.
4. Experience building research automation tools or working with LLM-based workflows.
5. Knowledge of scientific computing and statistical libraries such as SciPy, statsmodels or MLflow.