Merel Theisen is a Software Engineer working with the Kedro team at QuantumBlack, where she contributes to the open source framework that helps data science teams build reproducible, well-structured machine learning pipelines. Before joining the Kedro team, she worked on the company’s in-house experiment and model performance tracking tool, giving her hands-on insight into how ML teams evaluate and monitor their models in practice.

During her career as a software engineer, Merel has developed tools in various languages, including Java, Scala, and Python, for use cases ranging from social networking for academia to targeted advertising. That breadth of experience shows in how she approaches engineering for data science: with attention to maintainability, testing, and the real workflows of the people using the tools.

Merel has always been interested in machine learning and loves contributing to the field from a software engineering perspective. She really enjoys creating things and helping others, whether that is improving the developer experience of Kedro users or supporting the community around the framework.