AI-First Product Management
This course is designed to help learners build the product management skills companies are looking for today by blending proven PM practices with the power of AI.
Singapore
Serene has over 15 years of experience in the tech sector, and is currently the Head of Data Science for Transport, Marketplace and Experiences at Grab. She is responsible for managing the development of intelligent data-driven solutions for Grab’s transport and delivery services across South-East Asia. Her teams harness big data to develop and enhance Grab’s product offerings, in order to provide a delightful customer experience.
Prior to Grab, she started out her career as a defence scientist and system engineer at DSO National Laboratories, leading a team in applied research, modelling and operations research for large scale complex systems. She subsequently moved to Ernst & Young to build their new Advanced Analytics consulting practice, advising organisations on data strategy and implementing data science solutions for their business needs.
Serene is passionate about growing people in tech, and in particular empowering women to succeed in tech. She is the executive sponsor for Women@Grab, and an active mentor and speaker in topics related to data science, women in tech and STEM career development.
This course is designed to help learners build the product management skills companies are looking for today by blending proven PM practices with the power of AI.
This course is designed to prepare learners to use the most powerful Generative AI tools and apply the strategic frameworks needed to leverage them effectively and responsibly.
In this course, the learner will gain a hands-on introduction to data analytics and visualization using SQL and Tableau.
Join our global community of instructors and help shape the next generation of industry leaders — while moving your own career forward with proven subject matter expertise, leadership experience, and public speaking skills. Teach online or on campus, full-time or part-time.
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