Data Science Team Lifecycle Management [Updated: 11/21/2024]

Data scientists and engineers are some of the most sought-after roles in today’s job market. But many managers lack the complete set of skills required to recruit and retain key talent effectively.

In this course, Stanford University instructor and curriculum director Angel Evan offers a comprehensive overview of how to develop and manage diverse and inclusive data science and engineering teams. By the end of this course, you’ll be better equipped to navigate the three main stages of the employee lifecycle: recruiting, development, and retention.

Learning objectives

• Determine whether you need a data scientist and make the case for hiring one.
• Learn about the three core skills data scientists must have and common examples of projects they may work on.
• Get tips for successfully recruiting a diverse team and crafting an effective job description that avoids common mistakes.
• Review the major components of a good onboarding framework, including the understudy and surveyor approaches.
• Explore the key principles and challenges for managing in-house and remote data scientists for small to large companies and a three-layer approach for setting priorities.
• Provide the data scientists with the right skills and education to set them up for career advancement.
• Learn the fundamental differences between teaching and mentoring.
• Discover ways to identify and deal with the job burnout common among data scientists.
• Identify when to retain and promote employees or to part ways.