We are seeking a technically strong Data Scientist to join our Portfolio Performance Analytics team. You will play a key role in developing and applying quantitative models that enhance decision-making across Hayfin's investment strategies. The initial focus will be on Private Credit, where you will support portfolio management, risk assessment, and performance optimization. Over time, there will be opportunities to extend your work across Liquid Credit and Private Equity Fund of Funds strategies.

Key Responsibilities

1. Quantitative Model Development

• Design and implement machine learning and statistical models for portfolio analytics, risk evaluation, and alpha generation.
• Develop robust Python-based pipelines with clear documentation and test coverage.
• Work cross-functionally with investment, finance, risk, and technology teams to deploy solutions.

• Analyse large, complex datasets using SQL and Python to support investment research and operational insights.
• Integrate internal, market, and alternative data sources into modelling frameworks.
• Conduct exploratory analysis to inform investment decisions and identify signals.

• Apply supervised and unsupervised learning using frameworks such as JAX (preferred), TensorFlow, or PyTorch.
• Leverage scientific computing libraries (NumPy, Pandas) and visualization tools (matplotlib/seaborn).
• Optimize performance with vectorized operations or JIT compilation where appropriate.

• Develop models and tools tailored to private credit investment strategies, including direct lending, special opportunity portfolios.
• Collaborate closely with the Investment and Risk team to evaluate portfolio exposures, stress scenarios, and downside risk.
• Support financial analysis across various strategies, including analysis of instruments, financial statements, and risk metrics in the future.

• Maintain a high-quality model development process including version control (Git), documentation (Sphinx), and testing (pytest).
• Monitor model performance over time and suggest iterative improvements.
• Collaborate via workflow tools like JIRA to ensure agile delivery and coordination.

• Python: Strong programming experience with data science libraries such as NumPy, Pandas, matplotlib/seaborn.
• SQL: Comfortable querying large datasets and integrating results into Python workflows.
• Testing & Documentation: Familiarity with tools like pytest and Sphinx.
• Machine Learning: Hands-on experience with ML frameworks (preferably JAX, or TensorFlow/PyTorch) and core ML concepts.

• Bachelor's or master's degree in a quantitative field: Computer Science, Applied Mathematics, Engineering, Physics, or similar.

• Experience with numerical modelling in a financial or research environment.
• Exposure to portfolio modelling or analytics in Private Credit.
• Knowledge of fixed income instruments, credit risk, or portfolio construction principles.
• Familiarity with Git, JIRA, and collaborative development practices.
• CFA Level I or equivalent is a plus.