I am a Data Scientist at adidas in the Digital Data Science team in Amsterdam where I work on developing demand forecasting models and productionizing them in highly scalable, cloud-native data pipelines. Prior to joining adidas, I completed my Professional Doctorate (PDEng) in Data Science at Eindhoven University of Technology, where I worked on data science projects for multiple companies (ASML, Van Lanschot, TE Connectivity, Heijmans, FIOD). Before the PDEng, I conducted deep learning research focusing on anomaly detection in time series data and completed my Masters and Bachelors degree in Electrical & Computer Engineering.
I am broadly interested in the field of machine learning and its endless applications and passionate about turning cutting-edge research into products.
Here you can find a list of
my previous projects and here a list of publications.
Feel free to contact me!
March 2021I joined adidas as a Data Scientist in Amsterdam ///.
May 2020 Gave a talk on
"Anomaly Detection with Variational Autoencoders" (Video & Slides)
January 2020 Started my final project with
the Belastingdienst on object detection.
Gave a workshop on deep learning for the PDEng Data Science. Check out the materials (slides
In the Digital Data Science team, my main responsibilities have been:
Developing production-ready machine learning models that forecast demand for thousands of adidas products (PyTorch).
Building and maintaining highly scalable, robust, cloud-native data pipelines deployed on AWS (SageMaker Training, Inference, Processing, Pipelines; EMR; Step and Lambda functions)
Integrating MLOps components into the forecasting products (such as automated model monitoring)
Optimizing big data processing workflows (Spark, Hadoop)
Implementing SW engineering best practices (unit and integration testing, linting, CI pipelines, code reviews)
Working in an international team responsible for creating data products that provide better trading for adidas eCom business (.com and apps)
Jan. 2020 - Jan. 2021
In my 12-month-project with FIOD-Belastingdienst, I worked on the following tasks in collaboration with FIOD's data science team (CoDE):
Developed custom object detection and image classification models using TensorFlow and deployed them as REST APIs using Flask and Docker.
Professional Doctorate Candidate in Data Science
Jan. 2019 - Jan. 2021
Jheronimus Academy of Data Science (JADS)
In the PDEng Data Science I worked on 7 projects for several companies: 2x ASML, Van Lanschot, TE Connectivity, Heijmans. Check project here.
Coached/supervised students and professionals.
Lab Monitor (Volunteer)
Lisbon Machine Learning School (LxMLS)
Part of the organizing team of LxMLS'19 as a monitor. I had responsibilities in
the organization of the school and helped the students solving the exercises
during the lab sessions, implementing machine learning algorithms such as naive
Bayes, hidden Markov models, conditional random fields, recurrent neural
networks, and reinforcement learning. The school is mostly focused on Natural
Language Processing (NLP).
Jan. 2018 - Dec. 2018
Institute for Systems and Robotics (ISR-Lisbon)
Within the Signal and Image Processing group of ISR my research focused on deep learning and anomaly detection in time series data.
Proposed an approach for time series anomaly detection based on variational autoencoders, recurrent neural networks, and attention mechanisms. This approach is unsupervised, making it suitable for applications where obtaining labels is expensive or time-consuming (fraud detection, medical diagnosis, fault detection, ...).