Data Scientist – Financial Data & AI

About the Role

A leading investment management company is seeking a talented Data Scientist to join its expanding research and technology division. This role offers the opportunity to apply advanced data science and AI techniques to financial markets, helping to uncover insights, improve investment decisions, and enhance the firm’s analytical capabilities.

Key Responsibilities

• Analyse, structure and interpret large volumes of financial and alternative data to support investment research and portfolio management.
• Develop and apply machine learning and AI models for forecasting, trend detection, and scenario analysis.
• Design and implement robust data pipelines to ensure data quality, accessibility, and consistency.
• Collaborate with investment teams to translate analytical findings into actionable investment insights.
• Explore the use of modern AI tools, such as LLMs and deep learning architectures, to extract signals from complex and unstructured data sources.
• Present analytical outcomes through clear visualisations and reports for both technical and non-technical audiences.

Requirements

• Strong academic background in a quantitative or technical field (e.g. Data Science, Mathematics, Computer Science, Engineering, or Physics).
• Proven experience working with financial or economic data in a data science or research capacity.
• Proficiency in Python and key data science libraries (Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow).
• Practical knowledge of statistical analysis, time-series modelling, and machine learning methods.
• Ability to handle and interpret large, complex datasets from diverse financial sources.
• Strong communication skills with the ability to explain technical concepts clearly to investment professionals.

Desirable

• Experience with cloud computing platforms (AWS, Azure, or GCP).
• Exposure to portfolio optimisation, risk analysis, or systematic investment research.
• Familiarity with NLP, graph analytics, or generative AI in applied settings.
• Curiosity about financial markets and enthusiasm for innovation in data-driven investing.