Carlo Carandang

Building Data Science Capacity at Your New Job as an Enterprise Data Scientist

​Presentation Synopsis:  So you got a job as a data scientist at a large organization. But most data scientists work solo, and many organizations are just beginning their AI journey. So how do you build data science capacity? We all know how this ends up- you were hired to build data science capacity in your department, but you are not given well defined business use cases to solve, so you end up building AI prototypes in search of a business problem. And being one of the few data scientists in your whole organization, they assume you can just utilize their antiquated analytics stack, and you find out quickly your prototypes don’t leave your desktop, as there is no way to deploy your ML models at scale.​

​​Speaker Biography:  Carlo is a data scientist and the AI/ML Technical Lead of the Artificial Intelligence Centre of Excellence at the Canada Revenue Agency, where he guides and supports AI projects at Enterprise with AI governance, MLOps/ModelOps frameworks, data science best practices, and a data science service delivery model. Ultimately, Carlo is helping the Agency implement data science and Big Data technologies into a large government enterprise IT system. As the Technical Lead for the Enterprise AI/ML platform, he supports approximately 300 data scientists in an Enterprise organization with over 55,000 employees, ultimately serving all Canadians. Carlo has over 20 years experience as a researcher (clinical and data science), and has multiple publications, grants and presentations in the areas of data science, AI, psychiatry, and government.​​