Position: ML Researcher
Category: Data Science
English level: fluent
Years of experience: 2


As a Machine Learning Engineer, I created a custom AutoML Python package and used it to build a scalable fullfledged
AutoML Pipeline. The Pipeline automated feature generation & selection, hyperparameter tuning, evaluation & logging of the best models. It supported parallelized tuning and stacking and could be launched on-premise via Airflow or on the cloud via Kubeflow.

As a Machine Learning Researcher, I led the creation of a Multi-Task Graph Neural Network, by proactively researching GNNs & Multi-Task papers, organizing development and mentoring an ML Engineer. The developed Multi-Task approach was 100 times faster than the previous "Single Task - Single Model" and enabled the screening of billion-scale databases within hours. Afterwards, I researched papers on pre-training and Self-Supervised
Learning strategies for GNNs and GNN Transformers.