Requirements

• 3–5 years working with large-scale financial or scientific datasets.
• A Master's degree in a quantitative discipline (Mathematics, Statistics, Econometrics, Physics, or similar).
• 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.
• Genuine interest in financial markets (no prior knowledge or experience is required)

What You'll Be Doing

• Design statistical methods to detect outliers, distribution shifts, stale values, missing values, and inconsistencies across market datasets.
• Investigate anomalies, quantify their impact on pricing and trading decisions, and communicate findings through written analyses and design documents.
• Refactor and generalize methods as new asset classes and regions expose edge cases.
• Ingest, assess, clean and maintain historical data from various external sources.
• Engage researchers and traders directly on data trust, methodology, and trade-offs.
• Coordinate across curators, quantitative researchers, and developers for each dataset.
• Roll out new and existing methods to production across regions and asset classes.
• Contribute production code in shared libraries.

Perks and Benefits

• Research-driven trading firm environment.
• Opportunity to work with quantitative modeling, machine learning, and engineering.
• Contribute to shaping modern markets through technology and capital.
• Collaborative work culture among researchers, traders, and engineers.
• Rapid experimentation and real-time feedback for turning insights into execution.