Design statistical methods from first principles to detect outliers, distribution shifts, stale
Investigate anomalies, quantify their impact on pricing and trading decisions, and communicate
Refactor and generalise methods as new asset classes and regions expose edge cases
Ingest, assess, clean and maintain historical data from various external sources by designing and
Engage researchers and traders directly on data trust, methodology, and trade-offs
Coordinate across curators, quantitative researchers, and developers for each dataset

🎯 Requirements

Strong Python including the statistical libraries used for time-series analysis and surface fitting
Comfortable moving between interactive analysis and production code
A self-starter who finds the work that must be done, and does it without direction
Direct stakeholder communication with researchers and traders on data trust
Intellectual curiosity about messy real-world market data

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