About Me

I am a data scientist with a career built in academia as a physicist working at CERN. Recently, I have been focusing on advancing my knowledge and skills on Data Science and Machine Learning. I am looking for career opportunities to establish myself in the industry sector. I am enthusiastic about solving business problems and creatively using my skillset to add tangible value to the team. I am resilient, collaborative, and always looking to improve. In my free time, I enjoy reading, playing guitar, and coding fun projects.

Professional Experience

  • Academic Data Scientist École Polytechnique Fédérale de Lausanne (EPFL), 2017-2021
  • Ph.D student at European Center for Nuclear Research (CERN), 2014-2016

Education

Projects

Sorting Algorithms

Skills: Matplotlib, Numpy, Algorithms

Python script comparing famous sorting algorithms: Bubble Sorting, Insertion Sorting and Selection Sorting. The excellent matplotlib animation library allows us to see the algorithms in action sorting a 1D-array. It also provides the number of iterations performed while the sorting is taking place, and the total number of swaps performed.

Sentiment Analysis

Skills: Web Scrapping, ETL, Pandas, NLP, Matplotlib

A sentiment analysis of recent tweets is performed to evaluate if the overall emotion about a subject is negative or positive. The data is acumulated by web scraping 1000 recent tweets containing the terms 'covid', 'climate change' and 'crypto'. This can be a valuable tool to analyse the overall emotion of a product, service or brand given some textual reviews for example.

Exploratory Data Analysis

Skills: EDA, Pandas, Matplotlib, Numpy

In this project, I perform EDA on COVID-19 clinical trials data provided by ClinicalTrials.gov database. I investigate information about age groups, gender, location, number of participants, trials' status, and average time to complete the trials.

Linear Regression

Skills: EDA, Scikit-learn, Pandas, Matplotlib

What can we discover using Linear Regression? In this project, Linear Regression is used to determine the best features that define the best tennis players. The data provides information about the men’s professional tennis league from the top 1500 ranked players in the Association of Tennis Professionals.