Marcus Macedo
About Marcus Macedo
Marcus Macedo is a Python and AI Developer currently working at Turing in Palo Alto, California. He has a background in data science and engineering, with experience at Itaú Unibanco and the Aeronautics Institute of Technology.
Work at Turing
Marcus Macedo currently works at Turing as a Python/AI Developer. He has been in this role since 2023, contributing his expertise in artificial intelligence and Python programming. Turing is known for connecting companies with top tech talent globally, and Marcus's role involves developing AI solutions and enhancing data processing capabilities.
Previous Experience at Itaú Unibanco
Marcus Macedo has extensive experience at Itaú Unibanco, where he worked in various capacities. He served as a Data Scientist from 2019 to 2022 for three years, focusing on data analysis and model implementation. Prior to that, he was a Product Engineer Intern for seven months in 2018, and he also held the position of Data Analyst and Data Scientist Trainee for eight months in 2019.
Research Fellowship at Aeronautics Institute of Technology
From 2016 to 2018, Marcus Macedo worked as a Research Fellow at the Aeronautics Institute of Technology. In this role, he engaged in research activities that contributed to advancements in aerospace technologies. His experience at this institute laid a strong foundation for his subsequent work in data science and AI.
Educational Background
Marcus Macedo has a solid educational background in engineering and data science. He earned a Bachelor of Engineering in Aerospace Engineering from Universidade Federal do ABC from 2015 to 2019. He also completed a Bachelor in Science and Technology at the same university from 2013 to 2015. Additionally, he obtained a Postgraduate Degree in Data Science from Instituto Tecnológico de Aeronáutica in 2019.
Technical Skills and Expertise
Marcus Macedo possesses a range of technical skills in data engineering and machine learning. He is proficient in tools and platforms such as AWS, GCP, Apache Spark, and Airflow. His expertise extends to implementing machine learning models using frameworks like Pytorch, Tensorflow, and Keras. He is also experienced in MLOps practices, utilizing tools like Jenkins, Terraform, Docker/Kubernetes, and Kafka.