Requirements

• Minimum 2 years of relevant professional experience in Data Science, Data Analysis, Data Engineering or similar role within financial markets
• Practical understanding of financial markets and instruments, especially Macro, Fixed Income, Rates, Credit or related asset classes
• Strong Python programming skills, including experience with Pandas and NumPy
• Good SQL skills and experience with structured financial datasets
• Experience sourcing, analysing and processing financial data with focus on quality, validation and consistency
• Ability to understand financial concepts and communicate with Quantitative Researchers, Portfolio Managers, Traders and external vendors
• Strong analytical and problem solving skills for investigating unfamiliar datasets and finding solutions independently
• Familiarity with external data providers, financial data platforms and API integration
• Ability to manage competing priorities, take ownership of data initiatives, and collaborate across technical and business areas
• Degree in a quantitative discipline such as Mathematics, Statistics, Computer Science, Physics, Engineering or Finance; postgraduate qualification is beneficial

Nice to Haves

• Experience developing data pipelines, automating workflows or supporting production data infrastructure
• Knowledge of financial data concepts like yield curves, instrument reference data, market data and pricing conventions

What You'll Be Doing

• Collaborating with Quantitative Researchers, Portfolio Managers, and Traders to understand data requirements for research and trading strategies
• Evaluating existing data assets and external vendor offerings for coverage, quality, consistency and suitability
• Managing discovery, evaluation, and onboarding of new datasets focused on Macro markets including Rates, bonds, swaps, derivatives, and alternative data
• Engaging directly with external data providers to assess APIs, data availability, structure, and delivery mechanisms
• Developing Python tools and data pipelines to extract, clean, normalise, and analyse financial datasets
• Investigating discrepancies across data providers involving instrument identifiers, pricing conventions, timestamps, and reference data
• Maintaining and improving quality and consistency of existing datasets while proactively resolving issues
• Collaborating with engineering teams to automate data ingestion, validation, and integration for reliable delivery into research and trading environments

Perks and Benefits

• Inclusive and diverse workplace culture embracing equality and respect
• Initiatives and programs supporting a healthy work-life balance
• Opportunity to work at the intersection of financial markets, data science, and engineering