Natalia Tenorio Maia
About Natalia Tenorio Maia
Natalia Tenorio Maia is a Data Scientist with extensive experience in programming and Natural Language Processing. She has worked at various academic institutions and currently holds a position at Infinia ML, focusing on Machine Learning and Data Analysis.
Current Role at Infinia ML
Natalia Tenorio Maia has been employed as a Data Scientist at Infinia ML since 2021. In this role, she focuses on conducting machine learning and data analysis specifically for outage prediction. Her responsibilities involve utilizing advanced analytical techniques to enhance predictive capabilities within the organization.
Previous Experience in Research
Prior to her current position, Natalia worked as a Postdoctoral Research Scientist at the University of Pittsburgh from 2017 to 2020. She contributed to various research projects during her tenure. Before that, she served as a Graduate Student Researcher at São Paulo State University (UNESP) from 2011 to 2017, where she authored all the code for her research and projects.
Education and Expertise
Natalia holds a Doctor of Philosophy (PhD) in Physics from Universidade Estadual Paulista Júlio de Mesquita Filho, which she completed from 2011 to 2017. Additionally, she earned a Bachelor's degree in Physics from Universidade de Brasília, studying from 2007 to 2011. Her academic background provides a strong foundation for her expertise in natural language processing and intelligent document processing.
Technical Skills and Tools
Natalia demonstrates proficiency in several programming languages, including Python, Matlab, Mathematica, and Fortran. She utilizes tools such as Jupyter Notebooks, Excel, and Visual Studio Code in her work. Her technical skills are complemented by her involvement in client communication and root cause and failure analysis.
Research Roles and Contributions
Before her role at Infinia ML, Natalia worked as a Junior Researcher at Universidade de Brasília from 2007 to 2011. Her experience spans various research environments, contributing to her understanding of data science and machine learning applications in real-world scenarios.