Eliane Aguiar
About Eliane Aguiar
Eliane Aguiar is a data scientist currently working at Thoughtworks since 2024, with prior experience at various companies including Compass UOL, Stone Pagamentos, and Ambev Tech. She holds a Master's Degree in Mathematics Modelling from FGV and is pursuing an Executive MBA in Data Science & Analytics at USP/Esalq.
Work at ThoughtWorks
Eliane Aguiar has been employed at ThoughtWorks as a Cientista de Dados since 2024. She has worked remotely for a duration of five months. In her role, she focuses on data analysis and mathematical modeling, contributing to the company's data-driven projects.
Previous Experience in Data Science
Prior to her current position, Eliane Aguiar worked as a Cientista de Dados at Compass UOL for three months in 2024, also in a remote capacity. She has significant experience in data science, having held positions at Stone Pagamentos from 2017 to 2019, and at Ambev Tech from 2020 to 2022, both in São Paulo. Additionally, she worked at Shape for ten months from 2022 to 2023, further solidifying her expertise in the field.
Education and Expertise
Eliane Aguiar holds a Master’s Degree in Mathematics Modelling from FGV - Fundação Getulio Vargas, which she completed from 2014 to 2016. She is currently pursuing an Executive MBA in Data Science & Analytics at MBA USP/Esalq, expected to be completed in 2023. Her educational background in Applied Mathematics, which she studied at Universidade Federal do Rio de Janeiro from 2006 to 2012, provides a strong foundation for her work in data science.
Background in Technology
Before transitioning to data science, Eliane Aguiar gained experience in technology roles. She worked as a Programadora / Gerente de testes at Go2web from 2016 to 2017. Additionally, she was involved with COPPEAD UFRJ as a CEFIN member in 2010, where she contributed to financial studies. This diverse background has equipped her with a broad skill set applicable to her current data science endeavors.
Interests and Passion for Data Science
Eliane Aguiar has a strong passion for puzzles, algorithms, and logical thinking, which fuels her interest in data science. She aims to continuously learn and enhance her understanding of data retrieval, analysis, and the development of mathematical models. This commitment to ongoing education reflects her dedication to the field.