Ayokunle Abisola
About Ayokunle Abisola
Ayokunle Abisola is a Data Scientist specializing in AI Training, currently employed at Scale AI. He has a background in robotics and software engineering, with experience in developing machine learning models and algorithms.
Work at ScaleAI
Ayokunle Abisola currently holds the position of Data Scientist - AI Training at Scale AI. He has been with the company since 2023, contributing to various AI training initiatives. His role involves applying machine learning techniques to enhance AI models, ensuring they meet industry standards and requirements.
Previous Experience in Robotics and AI
Prior to his current role, Ayokunle worked at Robotics & Artificial Intelligence Nigeria (RAIN) as a Robotics ML Engineer from 2021 to 2022. He also served as a Graduate Trainee at RAIN from 2020 to 2021. His experience in these positions involved developing machine learning models and engaging in robotics projects.
Education and Expertise
Ayokunle Abisola earned a Bachelor of Technology (BTech) in Physics from the Federal University of Technology Akure, completing his studies in 2021. He further advanced his education by obtaining a Master of Science (MS) in Robotics and Autonomous Systems from the University of Lincoln in 2023. His academic background supports his expertise in machine learning and robotics.
Projects and Innovations
Throughout his career, Ayokunle has developed several significant projects. Notably, he created a malaria cell classifier using image analysis techniques and a facial expression recognizer utilizing deep convolutional neural networks. He also built a loan repayment predictor using artificial neural networks and designed a delivery drone as part of his project portfolio.
Experience as a Python Developer
Ayokunle worked as a Python Developer at Turing from 2022 to 2023. His role involved developing software solutions using Python, contributing to various projects remotely from California, United States. He also served as a Python Software Engineer at Slazor Limited from 2017 to 2021, where he worked in a hybrid environment.