Data science

I have 9+ years of experience processing data in high volumes and developing data science solutions within HP and the ATLAS Collaboration

Top skills, software & tools

Statistics and Machine Learning: Scikit-learn, TensorFlow, PyTorch, SciPy & statsmodel
Advanced Data Analysis: SQL & Python (PySpark, Pandas & NumPy)
Data Visualization and Data Analytics: Tableau, Looker Studio, Plotly & Matplotlib
Databricks
Git [GitLab and GitHub], Continuous Integration and automated testing (Pytest)
Docker
Kubernetes [Kubeflow]
Jupyter notebook
Microsoft Excel and Google Sheets

End-to-end development of Machine Learning solutions to improve sales across different business units
- Tech stack: python, PySpark, pandas, SQL and sklearn
Extraction of data-driven insights enabling decision making
Active participation in the interview process for new candidates, contributing to the selection of talented professionals who align with our team's goals

Performed several data analyses comprising:
- Data preparation and cleaning
- Precision measurements of physical quantities
- Various statistical analyses:
  - Extraction of data-driven corrections
  - Determination of uncertainties
  - Test statistic based on profile likelihood ratio for hypothesis testing
  - Chi-square goodness of fit test for hypothesis testing
- Successfully edited and published 5 scientific results
Implementation and deployment of a neural network to predict the position of a particle in a detector:
- Improved previous estimation by up to 60%
- Using NumPy, Pandas and Keras from tensorflow
Training and optimized an attention-based model to identify physical particles:
- Improved performance by up to 50%
- Using Docker and Kubernetes (Kubeflow pipelines and Katib)