Mariela Perignon
About Mariela Perignon
Mariela Perignon is a Team Lead at Arity in Chicago, Illinois, with a strong background in data science and environmental analysis. She has held various positions in academia and industry, including roles as a Postdoctoral Fellow and Program Director, and possesses expertise in machine learning, geospatial analysis, and programming.
Work at Arity
Mariela Perignon has been serving as a Team Lead at Arity since 2021. In this role, she is responsible for overseeing projects and guiding team members in the application of data science techniques. Her work is based in Chicago, Illinois, where she contributes to the company's focus on utilizing data to improve transportation and mobility solutions.
Education and Expertise
Mariela Perignon holds a Bachelor's degree in Geological and Earth Sciences/Geosciences from the Massachusetts Institute of Technology, which she completed from 2003 to 2007. She furthered her education with a Master's degree in the same field at MIT from 2007 to 2008. Additionally, she earned a Ph.D. from the University of Colorado Boulder, where she studied from 2008 to 2014.
Background
Mariela Perignon has a diverse professional background in data science and geosciences. She worked as a Postdoctoral Fellow at the University of Colorado Boulder from 2015 to 2017 and continued her postdoctoral research at The University of Texas at Austin from 2017 to 2019. She also held the position of Program Director for Data Science at Insight Data Science in 2020.
Technical Skills and Specializations
Mariela Perignon possesses a strong skill set in data science, particularly in natural language processing and deep learning. She is experienced in using geospatial analysis tools such as QGIS and ArcGIS, indicating her specialization in spatial data science. Her programming proficiency includes advanced skills in Python, SQL, and machine learning frameworks like TensorFlow and Keras.
Achievements in Data Science
Throughout her career, Mariela Perignon has developed software for spatial data analysis and numerical modeling. She has a comprehensive understanding of deploying machine learning models, especially in the context of environmental data analysis. Her passion lies in leveraging data science to explore the interactions between people and their environment.