About Cheryl

I am a data scientist.

Currently I am following a traineeship at Xomnia for which I am working 4 days a week at the Operations Research department at Airfrance KLM. There, I am working on several recommender systems in order to provide the customer data-driven and personal recommendations using Pyspark on an Hadoop cluster.

Besides my work and studies, I also have a great passion for travelling, animals & the environment, cooking, eating & drinking with friends & family, coffee, and my turtles.


Since July 2017 I am working as data science trainee for Xomnia and KLM. At the operations research department, specifically the customer data management team, I am doing data science projects in Spark using mainly Pyspark and Python. In the last year we successfully developed and put into production six recommender systems and one text mining project leading to great results in terms of click through rate and conversions of our customers. I work together with the scrum team using Git to do code reviews, pull requests and versioning of our code. Through Bamboo build plans we deliver our models to the production platform and ensure a continuous integration.

Before my current job I have also worked at the Digital Marketing department at Airfrance KLM and I worked as a product developer for Exsyn Aviation Solutions.


June 2017 I finished my master’s degree in Data Science at the University of Amsterdam for which I focused on machine learning of unstructured data and graduated in the digital marketing field at KLM.  For my thesis I developed an approach of clustering website visitors based on contextual information and data on their searched destination(s) in order to distinguish user profiles which reflect their similar interests and/or behaviour which can be used for further recommendations. This project was conducted mostly in Python using several machine learning algorithms.

I have a background in Aviation Engineering where I developed my interest in aircraft performance, aerodynamics, dynamic systems analyses & modelling and mathematics. In my final year I got accepted for the honours programme and got a taste for Data Science. Focusing my final year and graduation in this field, I wrote my thesis on predictive maintenance of aircraft components. Following the CRISP Data Mining methodology, I developed a predictive model in R which analyses and visualizes component replacement using text mining on maintenance logs and anomaly detection on error messages from equipment sensors. 

I have also completed several courses such as the Machine Learning course by Stanford University, the Cambridge Certificate in Advanced English and The Unix Workbench by Johns Hopkins University.