Data science

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

Technical skills

Software development:

Python for data modeling (Pandas and NumPy), data visualization (Matplotlib and Plotly), automated testing (Pytest) and machine learning and statistical methods (SciPy, statsmodel, Keras from tensorflow, Scikit-learn)
Business Intelligence / Analytics tools: Tableau and Looker Studio
Git [GitLab and GitHub] and Continuous Integration
Docker
Kubernetes [Kubeflow]
SQL
Jupyter notebook
Microsoft Excel and Google Sheets

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)