Ravi Bhanabhai
About Ravi Bhanabhai
Ravi Bhanabhai is a Senior NLP Scientist at Ofcom in Edinburgh, Scotland, with a strong background in machine learning and AI. He has held various roles in data science and engineering across multiple organizations, emphasizing the integration of classical engineering principles into AI applications.
Work at Ofcom
Ravi Bhanabhai has been employed at Ofcom as a Senior NLP Scientist since 2022. His role involves the application of natural language processing techniques to enhance communication technologies. Based in Edinburgh, Scotland, he contributes to projects that align with Ofcom's mission to ensure effective communication services across the UK.
Previous Experience in Data Science and Engineering
Prior to his current position, Ravi held multiple roles in the field of data science and engineering. He worked as a Chief Data Scientist at talkAItive from 2020 to 2022, and as a Data Scientist at the same company from 2017 to 2020. Additionally, he served as a Cloud Engineer at talkAItive for a brief period in 2021. His experience also includes a role as a Project Manager for the City of Toronto in 2021 and as a Design Engineer at Evertz from 2012 to 2013.
Education and Expertise
Ravi Bhanabhai holds a Master's Degree in Systems and Computer Engineering from Carleton University, which he completed from 2009 to 2011. He also earned a Bachelor of Engineering in Computer Engineering from Ryerson University between 2005 and 2009. His educational background supports his specialization in designing AI solutions, particularly in natural language processing.
Teaching and Lecturing Experience
Ravi has contributed to academic settings as a guest lecturer at the Schulich School of Business - York University in 2019 and 2020. His involvement in academia reflects his commitment to sharing knowledge in the fields of data science and AI, engaging with students to discuss contemporary topics in technology.
Focus on AI and Machine Learning
Ravi Bhanabhai is passionate about machine learning and AI. He actively engages in discussions surrounding these topics and encourages informal dialogues with peers. His work emphasizes the importance of creating stable, scalable, and efficient AI code, integrating classical engineering principles into modern AI applications.