Gabriel

Gabriel

Data Scientist | Astrophysicist

About Me

I am a Data Scientist with a strong foundation in Physics and a Master’s degree in Astrophysics from the University of São Paulo (USP). With over five years of experience, my passion lies in using machine learning, statistical modeling, and large-scale data analysis to solve complex problems and transform data into actionable insights.

From the Cosmos to Data: My Journey 🚀

My path to data science began with the stars. During my academic research in astrophysics, I wasn’t just observing the sky; I was tackling massive computational challenges. My work involved processing and analyzing over 100,000 galaxy spectra, which required building robust Python pipelines for data cleaning, feature extraction, and visualization.

I quickly learned that understanding the universe and understanding data are deeply connected. To uncover the secrets hidden in noisy astronomical signals, I applied advanced techniques like Bayesian inference, MCMC, and rigorous statistical modeling. This experience solidified my expertise in the entire data lifecycle—from wrangling raw information to developing predictive models and communicating results. I realized my core passion was not just astrophysics, but the process of solving complex puzzles with data, which led me to pursue a career in data science.

My Technical Approach 🛠️

My approach is built on a solid foundation of quantitative skills and a versatile technical toolkit.

  • Data Analysis & Statistical Modeling: I am highly skilled in Python and its core data science libraries (Pandas, NumPy, Scikit-learn). My day-to-day work involves crafting analytical models and performing exploratory data analysis to uncover underlying trends. My background in physics has given me a deep appreciation for rigorous statistical modeling and Bayesian inference, which I apply to extract meaningful insights from data.

  • Machine Learning & Experimentation: I have practical experience in applying various machine learning algorithms to solve problems. In a collaboration with an 8-member international team, I benchmarked 7 different ML and statistical methods to determine the most effective approaches for parameter estimation. I also have a strong understanding of A/B testing and experimental design, and I am currently expanding my deep learning skills with PyTorch and TensorFlow.

  • Tools & Environment: My preferred development environment is a Linux OS, using Git for version control and Jupyter Notebooks or Google Colab for analysis and prototyping.

Academic Background & Awards 🎓

My academic journey has been centered at the prestigious University of São Paulo (USP), where I have built my scientific and technical foundation.

  • PhD in Astronomy (in progress) - University of São Paulo (USP)
  • MSc in Astronomy - University of São Paulo (USP)
  • BSc in Physics - University of São Paulo (USP)

In 2024, I was honored to receive the Best Master’s Thesis Award from the XV Graduate Symposium at USP for my research work.

Beyond the Data 🌐

As a lifelong learner, I believe in clear communication and broad perspectives. I am a native Portuguese speaker and have advanced proficiency in English, particularly for technical writing and presentations. I also speak intermediate Spanish and basic German. Outside of work, I enjoy applying my problem-solving skills to other complex systems, whether it’s through strategy games or exploring new technologies.