Data Scientist
- 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.
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