What you'll be doing

• Collaborate with Quantitative Researchers, Portfolio Managers and Traders to understand data requirements and identify suitable datasets for strategies
• Evaluate existing data assets and external vendor offerings focusing on coverage, quality, consistency and suitability
• Manage discovery, evaluation and onboarding of new datasets, especially related to Macro markets and various financial instruments
• Engage directly with external data providers to assess offerings, APIs, data structure and delivery mechanisms
• Develop Python tools and data pipelines to extract, clean, normalize, and analyze financial datasets
• Investigate discrepancies across data providers including differences in identifiers, pricing, timestamps, and reference data
• Maintain and improve quality and consistency of existing datasets while proactively resolving issues
• Collaborate with engineering teams to automate data ingestion, validation and integration for research and trading environments

Requirements

• Minimum 2 years 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 with experience in data analysis libraries like Pandas and NumPy
• Good SQL skills and experience working with structured financial datasets
• Experience sourcing, analyzing and processing financial data with focus on data quality and validation
• Ability to understand financial concepts and communicate effectively with technical and business teams including external vendors
• Strong analytical and problem-solving skills to investigate unfamiliar datasets 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 functions
• Degree in a quantitative discipline such as Mathematics, Statistics, Computer Science, Physics, Engineering or Finance

Nice to haves

• Postgraduate qualification in relevant quantitative discipline
• Experience developing data pipelines, automating data workflows or supporting production data infrastructure
• Knowledge of financial data concepts such as yield curves, instrument reference data, market data and pricing conventions

Perks and benefits

• Equal opportunity employer welcoming diversity as essential to success
• Culture empowering employees to work openly and respectfully for collective success
• Initiatives and programs to support a healthy work-life balance