Get To Know Our Visiting Lecturers: Jiaxi Zhu
Jiaxi Zhu
Jiaxi Zhu
Visiting Lecturer, Career Foundations and Engagement (IND ENG 298)
Teaching beginning the week of December 3
Head of Analytics, Google (SMB)
This fall 2025, industry experts join the Master of Analytics program to teach IND ENG 298: Career Foundations & Engagement, helping students build the skills and confidence needed to navigate internships, job searches, and early-career growth.
Meet our Fall visiting instructor, Jiaxi Zhu, a machine learning and AI expert who translates technical insights into executive-level decisions.
About Jiaxi
Jiaxi Zhu brings over a decade of experience in business analytics and currently serves as Head of Analytics for Google’s Small and Medium Business (SMB) division. He leads global teams in applying advanced analytics and AI-driven models to inform strategy, forecasting, and customer experience for millions of businesses.
Previously, Jiaxi advised Fortune 500 executives at McKinsey & Company and PwC on data strategy and digital transformation. He also mentors startups through Google for Startups Accelerator and serves as a judge for analytics and AI competitions.
Jiaxi holds an MBA from Wharton with dual majors in Business Analytics and Entrepreneurship & Innovation, and a B.S. in Industrial Engineering & Operations Research from UC Berkeley.
Why I’m teaching this course
“I’m teaching this course to help students gain a practitioner’s understanding of the full analytics lifecycle—from designing frameworks and building models to communicating insights effectively. My goal is to equip students with both the technical and strategic skills to lead high-impact analytics initiatives.”
Q&A with Jiaxi Zhu
What inspired you to teach at UC Berkeley, and what’s one concept you hope really “clicks” for your students?
Returning to UC Berkeley as an instructor, I hope to provide an independent perspective on how classroom concepts could unfold in the industry. The main concept I hope “clicks” for students is “narrative-ready analytics”. In my projects, I’ve seen technically sophisticated models fail to make an impact because they were not set up for executive decision making. The point of view I hope to share with students is to focus on both analytical rigor and decision-making clarity, so that they feel confident about both their analytical skills and ability to guide teams to make informed choices.
How do you see AI and machine learning transforming analytics roles in the next few years?
AI will automate a huge portion of the technical workload, such as writing queries, processing data, and generating preliminary insights. This presents a significant opportunity for analysts to step up from the role of a data provider into a strategic problem solver. As AI simplifies complex analyses, the human edge (i.e. business judgment, strategic influencing) becomes the most valuable skill.
What’s one piece of advice you’d give to students pursuing careers in data science and analytics?
Models are important and students should definitely ensure that they master the foundational skills. But that alone is not enough. As data scientists and analysts, we should aim to shape decisions. The most successful analysts are the ones who can translate their insights into impact. Always seek to understand the “so what”, and push for decision clarity, and not analytical complexity.