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Sebastian Urbina
Vice President at Genpact specializing in Artificial Intelligence, Machine Learning, and Data Science
Sebastian Urbina's Career
Sebastian Urbina is currently the Vice President of the Artificial Intelligence, Machine Learning, and Data Science Team at Genpact in Costa Rica.12 In this role, he coordinates multiple aspects of the company's forecast engagements, from solution design to technology selection and implementation.1
Prior to joining Genpact, Urbina worked as a Director at Barkawi Management Consultants from 2016 to 2019.2 He also served as the Vice Minister of Transportation and Road Safety for the Costa Rican Ministry of Public Works and Transportation from 2014 to 2016.2
Urbina has a diverse educational background, holding degrees from several top universities:
- Master of Engineering in Financial Mathematics from The Johns Hopkins University (2022-2024)2
- Master of Information and Data Science from UC Berkeley (2021-2022)23
- Master of Science in Industrial Engineering from Georgia Tech (2005-2006)2
- Bachelor of Science in Industrial Engineering and Economics from Georgia Tech (2001-2004)2
Urbina's Expertise and Passion
Urbina is passionate about constantly learning and finding creative ways to solve complex problems.1 He has helped companies in various industries, such as cosmetics, consumer electronics, and Formula E racing, by applying techniques like forecasting, simulation, and optimization.1
Urbina believes that the ability to communicate technical concepts to non-technical stakeholders is crucial for success in AI and machine learning.1 He stays current by reading books, engaging with online resources, and reviewing the latest techniques and tools in the field.1
Urbina's Contributions and Achievements
In addition to his corporate roles, Urbina has made significant contributions to the field of AI and data science:
- He was the Director of the Trade, Innovation and Productivity (TIP) Center at Georgia Tech in Costa Rica from 2009 to 2010.2
- Urbina has been recognized as a thought leader in the industry, with his LinkedIn profile being featured in a post thanking him for his contributions.4
Overall, Sebastian Urbina is an accomplished professional with extensive experience in AI, machine learning, and data science. His diverse background, passion for learning, and ability to communicate complex ideas make him a valuable asset to Genpact and the broader industry.
Highlights
If you are at Gartner for Supply Chain symposium, join our session at 2:30pm today so you hear about the latest in #forecasting best practices and how to work together across #supplychain teams.
I forecast fun times. 🔮
GenAI in Supply Chain: Improve your data capture:
“We stocked out of biggest seller. What happened?”
A question heard often in supply chain meetings and discussions. The answers can be as varied as a promotion we ran was very successful and demand was beyond our expectations or manufacturing plants were overburdened and we couldn’t produce enough or one of our suppliers missed a delivery window delaying our whole replenishment process.
The reasons for not capturing and systematizating the data are varied, but in general it takes effort, there is no easy way of doing it, and the team is usually to busy focused on addressing the issue to make sure it is appropriately captured.
It is here than Generative AI plays a role.
Through Large Language Models and Generative AI its possible to monitor all these conversations and chats for the events. The system identifies the event when it happens, monitors how long it takes to resolve, how many people were involved, what type of event it was (reason code) and how it was resolved. It can then systematize the data, convert to a structured table and store it a data lake. All of this with minimal input from the team.
With this new rich data, a continuous improvement or analytics team can come in and start looking for trends. How often are things caused because of a bad forecast, inaccurate promotion, inefficient supply? It can adjust the history so that the forecast is not affected by past problems that have now been resolved. Programs to address underlying issues can be implemented.
Generative AI becomes a tool not just to understand the world but to understand ourselves. To drive the internal improvement leading to productivity enhancements.
