AI Career Mentorship Program

Dr. Su Huang has extensive experience applying analytics, data science, economics, and AI-driven thinking to real-world business and organizational challenges across technology, consulting, education, and product strategy. He is currently working with an AI startup to help clients identify, structure, and solve practical business problems using AI and analytical platforms.

Previously, he worked as an Economist Manager at Amazon, where his work focused on decision-making, forecasting, experimentation, pricing strategy, supply chain optimization, marketing analytics, and people analytics. As the first economist/data scientist supporting Amazon Fresh, he helped design and scale analytical science models and pipelines that contributed to more than $10M in business value across pricing, location strategy, and operational decision-making.

Before joining Amazon, he also gained experience in marketing analytics and consulting-related work, helping organizations better connect data-driven insights with business strategy and decision-making. His broader background across analytics, business communication, and stakeholder management provides a practical perspective on how technical work creates real organizational impact.

In addition to technical and business leadership responsibilities, he also managed the Amazon Economist Mentorship Program, helping junior economists transition from academic research environments into fast-paced industry settings. Throughout his career, he has mentored and managed many semi-technical professionals (such as Business Intelligence Engineers) and helped them successfully transition into more technical and analytical roles such as Data Scientist and Applied Scientist positions. Furthermore, he has successfully referred more than 10 professionals to roles within his own organization and has also helped more than 100 individuals explore and secure career opportunities through his broader professional network and mentorship.

Beyond industry experience, Su also teaches business analytics and quantitative problem solving at the university business school level. He also teaches elementary and middle school students through after-school and summer camp programs focused on real-world problem solving, analytical thinking, and practical AI awareness for the next generation.

ABC XYZ is now a director of META with extensive experience leading large-scale AI, machine learning, and data science initiatives across major technology organizations. Her background includes leadership roles in product analytics, AI-driven business strategy, machine learning platforms, and organizational transformation across cross-functional engineering, product, marketing, and business teams.

Throughout her career, she has led high-performing teams, driven data-informed decision making at scale, and helped organizations apply AI and analytical thinking to solve practical product and operational challenges in rapidly evolving technology environments. Her experience coordinating across technical and business functions provides valuable perspectives on how modern AI initiatives succeed in real-world organizations.

Years of experience building large-scale distributed systems at top technology companies. Brings deep expertise in architecture, code quality, and engineering best practices across backend infrastructure, APIs, and cloud-native platforms.

Has designed and shipped production systems handling millions of requests, mentored engineering teams through complex technical challenges, and helped organizations adopt modern development practices including CI/CD, microservices, and AI-augmented engineering workflows.

Experienced in guiding engineers through career growth from junior to senior and staff-level roles, with a practical understanding of how technical excellence, communication, and system thinking combine to create outsized impact in engineering organizations.

Built and deployed production machine learning models at a top FAANG company. Expert in turning research into real-world AI systems that scale, with deep experience across natural language processing, recommendation systems, computer vision, and generative AI.

Has published research, shipped ML systems serving millions of users, and bridged the gap between academic innovation and production-grade engineering. Experienced in the full ML lifecycle — from problem framing and data strategy through model development, evaluation, deployment, and monitoring.

Passionate about helping professionals transition into applied science and ML engineering roles, with practical guidance on building the right combination of research depth, engineering skills, and business impact to succeed in industry AI roles.