AI Project Coordination for Lead Data Scientists

Project coordination is the one hardest task for technical leads. At mid-career, the focus shifts from not just being a technical expert but a leader who can scope, plan, and manage AI projects. It gives lead data scientists the chance to shape strategic outcomes beyond technical work. In this course, instructor Matthew Blasa teaches you project management skills that are often learned through difficult trial and error. Matthew covers high-level questions like: What makes AI project management different from traditional project management? Who are the essential stakeholders in AI projects and how do you align them? How do you properly scope an AI project considering feasibility, value and resources? What are the most common reasons AI projects fail during deployment? And many more.

Learning objectives
Analyze the unique characteristics of AI project lifecycles that differentiate them from traditional software projects, including research phases, proof of concept development, and productionization requirements.
Evaluate data dependencies, quality issues, and governance requirements to develop comprehensive AI project plans that address technical, legal, and organizational risks.
Design effective team structures for AI delivery by allocating appropriate roles across data scientists, data engineers, and machine learning engineers while accounting for iteration-heavy work.
Develop success metrics that effectively connect model performance to business goals, enabling proper communication of AI project progress to both technical teams and executives.
Apply strategies for managing AI project challenges, including timeline estimation, budget defense, scope adjustment, and when necessary, project pivot or shutdown.