Daniela Flores Villanueva
About Daniela Flores Villanueva
Daniela Flores Villanueva is a Software Engineer with a Bachelor of Science in Computer Science and a Master of Engineering Sciences from Pontificia Universidad Católica de Chile. She has extensive experience in software development and machine learning, currently working at Fintoc and serving as a Teaching Assistant at her alma mater.
Work at Fintoc
Daniela Flores Villanueva has been employed at Fintoc as a Software Engineer since 2021. In this role, she focuses on backend development, utilizing frameworks such as FastAPI and Django. Her work involves the implementation of software solutions that enhance the company's financial technology offerings. Daniela's expertise in machine learning and data visualization contributes to the development of innovative applications within the organization.
Education and Expertise
Daniela studied at Pontificia Universidad Católica de Chile, where she earned a Bachelor of Science in Computer Science from 2014 to 2019. She furthered her education by obtaining a Master of Engineering Sciences, specializing in Machine Learning, Deep Learning, and Data Visualization, from 2019 to 2021. Daniela has developed expertise in various programming and data manipulation tools, including scikit-learn for machine learning model development, TensorFlow and PyTorch for deep learning, and Pandas and Numpy for data analysis.
Background
Before joining Fintoc, Daniela held several software engineering positions. She worked at Houm for two years from 2019 to 2021, where she contributed to software development projects. Prior to that, she was employed at Varus for three months in 2019 and completed a software engineering internship at Unholster for one month in the same year. Additionally, she worked at YOM for eight months in 2021. Daniela has also been a Teaching Assistant for Introduction to Programming at Pontificia Universidad Católica de Chile since 2015.
Technical Skills
Daniela possesses a range of technical skills relevant to software engineering and data science. She is proficient in using FastAPI and Django for backend development, and she employs TensorFlow and PyTorch for deep learning model implementation. For data visualization, she utilizes tools such as Matplotlib and Seaborn. Her skills in data manipulation and analysis are supported by her proficiency in Pandas and Numpy, which she incorporates into her projects.