
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:
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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)
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Business Intelligence / Analytics tools: Tableau and Looker Studio
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Git [GitLab and GitHub] and Continuous Integration
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Docker
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Kubernetes [Kubeflow]
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SQL
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Jupyter notebook
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Microsoft Excel and Google Sheets
Highlights

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

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Machine Learning, Data Science and Deep Learning with Python [October 2022]
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AWS Essentials [March 2023]
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Business Analysis Fundamentals [March 2023]

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Develop the Skills to Drive Innovation in Your Organization [June 2023]
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Learning Cloud Computing: Core Concepts [July 2023]
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Learning SQL programming [August 2023]
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Intermediate SQL for Data Scientists [August 2023]
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Tableau for Data Scientists [November 2023]
Other
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Machine Learning Summer School [June 2018]