Cainã is a data scientist from Brazil. As a research associate, he joined the Nelson Institute Center for Sustainability and the Global Environment (SAGE) to support database development, perform data analysis, and communicate findings to stakeholders.

Cainã holds a bachelor's, master's, and PhD in science. During his PhD, he applied advanced data mining techniques to detect signals of natural selection in native populations from South America, including the Amazon Rainforest and the Andean highlands. His research highlighted genes essential to local adaptation, such as the protective role of specific genetic variants against Chagas disease.

After his PhD, Cainã earned an MBA in Data Science & Analytics and has worked as a data scientist for startups and global companies for four years. He has built and deployed machine learning models to deliver end-to-end AI solutions, including diagnostic and predictive models, time series forecasting, and computer vision applications.

Cainã has also worked as a teacher and mentor for data scientists, contributed to Python open-source packages like feature-engine, and spoken at conferences such as PyData.

In his free time, he enjoys spending time with his family, including his dog and cat, engaging in outdoor activities with friends, and watching TV shows.