Fangfang Lee

As an AI Consultant, I work with Google’s strategic customers on critical projects to help them transform their businesses using AI/ML technologies. I help customers to develop, deploy, and manage custom (Gen) AI solutions in production using Google Cloud technologies. I provide technical project management, consulting, and technical aptitude to customer engagements while working with client executives and key technical leaders cross-functionally to deploy solutions.

• Lead a team of 4-5 engineers and act as technical lead to scope, manager, and delivery end-to-end implementation of Cloud Generative AI Solutions leveraging Gemini and other LLMs to 8 distinct customers across 12 projects, resulting in $X million in revenue
• Work with internal specialists, product and engineering teams to package best practices and lessons learned into thought leadership, methodologies, and published assets "Delivery Navigator"
• Interact cross-functionally with sales, partners, and customer technical stakeholders to manage project scope, priorities, deliverables, risks/issues, and timelines for successful client outcomes, securing 100% CSAT across all engagements
• Advocate for customers needs to overcome adoption blockers and drive new feature development based on the field experience.
• Propose solution architectures, manage the deployment of cloud based AI/ML solutions according to complex customer requirements and implementation best practices.

• Build Flatiron School inaugural Data Science Program curriculum on from scratch, designing and creating over 100 unique and scalable lessons ranging from Linear Regression to Image Classification
• Strategically lead transition plan from in-person to remote, delivering technical and non-technical content via Zoom and holding weekly staff meetings to discuss progress and feedback
• Work cross-functionally post-restructure as both a lecturer, a data science content creator, and project manager

• Deliver over 200 unique lectures and presentations to non-technical students which clearly explain both underlying intuition of Machine Learning algorithms and technical underpinnings
• Teach over 30 students in one cohort per quarter, resulting in over 250 graduates in Data Science

• Participate in 100 hours of training and workshops in text mining, web-scraping, Git/Github collaboration, machine learning and other data science courses offered by university faculty
• In collaboration with National Science Foundation, Center for Sciences and Engineering Statistics (NSF) to research and web-scrape over 5000 articles in various trade journals, magazines, and open-source data, and prepare for analytics and ML models. Applied Random Forests and Naive Bayes to predict which article is likely to produce innovation