Experience

Robbyson Systems

Mid-Level Data Scientist (May/2024 - present)

  • Designed proof of concept applications leveraged by LLMs for information summarizaiton in textual, video and audio formats.
  • Designed and implemented a Python package for data analysis on GitLab repositories data to assess efficiency and development cost.
  • Analyzed data from behavioral projection algorithms in order to ensure quality and consistency.

Big Data S/A

Junior Data Scientist (Oct/2022 - Mar/2024)

As a Junior Data Scientist, I transitioned to the Forecast and Product Operations divisions, where I developed and maintained Machine Learning-centered pipelines.

  • Contributed to the development of a recommendation system for a market-leading company in the civil construction sector.
  • Idealized and implemented a quality assurance framework responsible for automating artifacts validation processes.
  • Contributed to codebases with new features, code reviews, maintenance, refactoring and testing using the GitLab ecossystem.
  • Conducted and presented data analyses regarding both client data and deliverables.
  • Orchestrated ETL and Machine Learning pipelines with Apache Airflow.
Data Science Intern (Jan/2022 - Sep/2022)

Developed a price-elasticity modeling approach through Panel Data Regression and Supervised Machine Learning for products of a market-leading company in the civil construction sector using Python statsmodels and pandas. This technique was applied to models in production to incorporate the behavior of market price dynamics and adjust item recommendations.

FAPESP - Fundação de Amparo à Pesquisa do Estado de São Paulo

Researcher in Applied Machine Learning (Sep/2020 - Sep/2021)

As a FAPESP researcher, I developed under the guidance of Prof. Renato Fernandes Cantão, a scientific initiation project focused on Machine Learning applications in the context of photovoltaic solar energy. During this project, machine learning-based modeling strategies were developed and implemented to predict solar radiation in locations in the State of São Paulo, Brazil.

Check out the project page for more information.