Data scientist with +3 years of experience in various sectors such as construction, food/clothing retail, Agro, involving problem solving/projects through forecasting, clustering, classification, NLP, GenAI. Previously, working as a Business Analyst with +6 years of experience. Check out my resume here for a detailed description of the tasks and deliveries performed.
Currently a Master's student in Computer Science at UFS, I graduated with an MBA in Business Management and Competitive Intelligence from Universidade Tiradentes (2020) and a Bachelor's degree in Administration from Faculdade São Luís de França (2019).
My research focus is artificial intelligence (AI), specifically language models. My work explores Natural Language Processing (NLP) techniques applied to (but not limited to) healthcare, medical and biomedical domains to improve processes, reduce cognitive load on professionals and information overload through methods such as Text Summarization and Named Entity Recognition (NER). In addition, I am interested in Commonsense Reasoning in language models and also Optimization.
This section lists some technologies that I master and have experience with.
- Languages: Portuguese (Native), English (B2).
- Programming Languages: Python, SQL.
- Machine Learning Tools: Scikit-learn, Pytorch, Tensorflow, CatBoost, NumPy, Pandas, Seaborn, Plotly, PySpark, Databricks.
- Technologies and Skills: Data acquisition, processing and modeling, data visualization with tools (Eg. Power BI), Regression, Classification, Optimization, Bioinspired Optimization, Time Series Analysis, Forecasting, Hypothesis Testing, Excel.
- Cloud: Databricks, AWS, GCP, Azure (Development to Deployment)
This section lists some side projects created with the aim of exploring new technologies, algorithms and models, that is, to learn.
- Blog (Medium) in Portuguese and Multilingual
- Time Series Forecasting of Cryptocurrencies
- Building Generative AI Applications with Gradio
- Retail Topic Modelling with BERTopic
This section lists some repositories on topics of interest for study. These repositories contain notes, summaries, index cards and personal insights on studies. It does not contain derived materials.