Vlad Orlov
About Vlad Orlov
Vlad Orlov is a Machine Learning Engineer with extensive experience in developing predictive algorithms and machine learning systems. He has held various roles at notable companies including PayPal, Capital One, and Apple, and currently works at First Resonance.
Work at Resonance
Currently, Vlad Orlov serves as a Machine Learning Engineer at First Resonance. He has been in this role since 2023, contributing to the development of machine learning solutions within the organization. His responsibilities include designing and implementing machine learning models and data pipelines to enhance operational efficiency.
Previous Experience in Machine Learning and Data Science
Vlad Orlov has extensive experience in the field of machine learning and data science. He worked at PayPal as Team Lead for Machine Learning from 2019 to 2022, where he led a team focused on developing machine learning applications. Prior to that, he was a Data Scientist at Capital One from 2016 to 2019, and a Machine Learning Engineer at Sungevity from 2015 to 2016. His role at Oplus.ai as Founding Engineer, Machine Learning from 2022 to 2023 further solidified his expertise.
Education and Expertise
Vlad Orlov holds two Master's degrees. He studied Computer Science at Taurida 'V. I. Vernadskiy' National University from 2004 to 2009. He later pursued a Master's degree in Data Science at Southern Methodist University from 2017 to 2019. His educational background supports his expertise in machine learning, predictive algorithms, and data analysis.
Background in Software Engineering
Before specializing in machine learning, Vlad Orlov gained experience as a Software Engineer at Apple from 2013 to 2015. He also held the position of Engineering Team Lead at Codeminders/Tristero Consulting from 2009 to 2015. His background includes leading technical development for projects that involve constructing end-to-end machine learning and data pipelines.
Research and Development in Machine Learning
Vlad Orlov has engaged in research focused on new predictive algorithms within various domains, including AdTech, generative AI, and machine perception. His work emphasizes the application of advanced machine learning techniques to solve complex problems and improve system performance.