About the role

Design and implement statistical methods to detect and quantify issues in large-scale financial datasets. Manage automated pipelines for historical data ingestion and collaborate with researchers and traders to ensure data integrity.

What they look for

Python Statistical analysis Time-series analysis Surface fitting Data quality Data pipelines Financial datasets Statistical methods Anomaly detection Data curation Quantitative analysis Market data Production code Stakeholder communication

Requirements

Requires a Master's degree in a quantitative discipline and 3-5 years of experience working with large-scale financial or scientific datasets. Strong proficiency in Python and statistical libraries for time-series analysis is essential.

Full description

At IMC, data shapes trading decisions across a wide range of financial products. The Data Quality team owns the integrity, completeness, and timeliness of the datasets our Quantitative Researchers and Traders rely on. As a Data Quality Engineer, you design the statistical methods that detect, characterise, and quantify issues in those datasets. Additionally, you own historical datasets that turn raw vendor and internal feeds into pricing-grade inputs and output.

Your responsibilities

Statistical methods

Design statistical methods from first principles 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 generalise methods as new asset classes and regions expose edge cases.

Historical Datasets

Ingest, assess, clean and maintain historical data from various external sources by designing and owning automated pipelines for corrections

Engage researchers and traders directly on data trust, methodology, and trade-offs.

Coordinate across curators, quantitative researchers, and developers for each dataset.

Curation in production

Roll out new and existing methods to production across regions and asset classes.

Contribute production code in shared libraries

What you bring to the team

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.

About Us

IMC is a global trading firm powered by a cutting-edge research environment and a world-class technology backbone. Since 1989, we’ve been a stabilizing force in financial markets, providing essential liquidity upon which market participants depend. Across our offices in the US, Europe, Asia Pacific, and India, our talented quant researchers, engineers, traders, and business operations professionals are united by our uniquely collaborative, high-performance culture, and our commitment to giving back. From entering dynamic new markets to embracing disruptive technologies, and from developing an innovative research environment to diversifying our trading strategies, we dare to continuously innovate and collaborate to succeed.