Role at a glance
- Salary
- Not Disclosed
- Location
- Amsterdam, Netherlands
- Work arrangement
- On-site
- Employment
- Full-time
- Experience
- 3–5 years working with large-scale financial or scientific datasets.
- Education
- A Master's degree in a quantitative discipline (Mathematics, Statistics, Econometrics, Physics, or similar).
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Role Summary
The Data Quality Engineer designs statistical methods to detect, characterize, and quantify issues in market datasets. The role also owns historical datasets, turning raw vendor and internal feeds into pricing-grade inputs and outputs.
What You'll Do
- Design statistical methods to detect outliers, distribution shifts, stale or missing values, and inconsistencies across market datasets.
- Investigate anomalies, quantify their impact on pricing and trading decisions, and communicate findings through written analyses and...
- Refactor and generalize methods as new asset classes and regions expose edge cases.
- Ingest, assess, clean, and maintain historical data from external sources using automated correction pipelines.
- Engage researchers and traders on data trust, methodology, and trade-offs, and coordinate with curators, quantitative researchers, and...
- Roll out methods to production across regions and asset classes, and contribute production code to shared libraries.
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
3–5 years working with large-scale financial or scientific datasets; a Master's degree in a quantitative discipline; strong Python, including statistical libraries used for time-series analysis and surface fitting; comfort moving between interactive analysis and production code; direct stakeholder communication with researchers and traders on data trust; genuine interest in financial markets (no prior knowledge or experience is required).
Original job description
Content provided by the employer
Original job description
Content provided by the employer
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.
- Genuine interest in financial markets (no prior knowledge or experience is required)
About Us
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.
About the company
IMC Trading
Large Enterprise
IMC Trading is a global proprietary trading firm that leverages technology and quantitative research to optimize trading strategies across a variety of asset classes, including equities, commodities, and derivatives. Founded in 1989 and headquartered in Amsterdam, IMC operates with a commitment to innovation and collaboration, fostering an environment where talented professionals can thrive. The firm is dedicated to responsible trading practices and actively engages in market-making to ensure liquidity and efficiency in financial markets worldwide. With a focus on continuous improvement and data-driven decision-making, IMC Trading is a leader in the fast-paced world of trading and investment.