How to Lead in Data Science

How to Lead in Data Science

by Jike Chong, Yue Cathy Chang
Released December 2021
Publisher(s): Manning Publications
ISBN: 9781617298899

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Book description

A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples.

In How To Lead in Data Science you will learn:

  • Best practices for leading projects while balancing complex trade-offs
  • Specifying, prioritizing, and planning projects from vague requirements
  • Navigating structural challenges in your organization
  • Working through project failures with positivity and tenacity
  • Growing your team with coaching, mentoring, and advising
  • Crafting technology roadmaps and championing successful projects
  • Driving diversity, inclusion, and belonging within teams
  • Architecting a long-term business strategy and data roadmap as an executive
  • Delivering a data-driven culture and structuring productive data science organizations

How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas.

About the Technology
Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite.

About the Book
How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself.

What's Inside
  • How to coach and mentor team members
  • Navigate an organization’s structural challenges
  • Secure commitments from other teams and partners
  • Stay current with the technology landscape
  • Advance your career


About the Reader
For data science practitioners at all levels.

About the Authors
Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies.

Quotes
Spot-on as a career resource! Captures what’s important to be successful as a data scientist.
- Eric Colson, Former Data Executive at Stitch Fix, Netflix

The first-of-its-kind book to discuss data science career development in a systematic way! Highly valuable and timely in a world that generates more and more data!”
- Michael Li, VP of Data at Coinbase

A valuable reference filled with new and useful coaching and techniques. A must-have.
- Jesse Bridgewater, VP Data Science at Brightline, formerly Livongo, Twitter, eBay

A great book providing frameworks and tools that help contemplate and address key problems faced by data science leaders.
- Ron Kohavi, Best-selling Author, Former Executive at Airbnb, Microsoft, Amazon

Table of contents

  1. inside front cover
  2. How to Lead in Data Science
  3. Copyright
  4. dedication
  5. brief contents
  6. contents
  7. front matter
    1. foreword
    2. preface
      1. References
    3. acknowledgments
    4. about this book
      1. Who should read this book
      2. How this book is organized
      3. Self-assessment and development focus
      4. Case studies
      5. Gem insights
      6. liveBook discussion forum
    5. about the authors
    6. about the cover illustration
  8. 1 What makes a successful data scientist?
    1. 1.1 Data scientist expectations
      1. 1.1.1 The Venn diagram a decade later
      2. 1.1.2 What is missing?
      3. 1.1.3 Understanding ability and motivation: Assessing capabilities and virtues
    2. 1.2 Career progression in data science
      1. 1.2.1 Interview and promotion woes
      2. 1.2.2 What are (hiring) managers looking for?
    3. Summary
    4. References
  9. Part 1. The tech lead: Cultivating leadership
  10. 2 Capabilities for leading projects
    1. 2.1 Technology: Tools and skills
      1. 2.1.1 Framing the problem to maximize business impact
      2. 2.1.2 Discovering patterns in data
      3. 2.1.3 Setting expectations for success
    2. 2.2 Execution: Best practices
      1. 2.2.1 Specifying and prioritizing projects from vague requirements
      2. 2.2.2 Planning and managing data science projects
      3. 2.2.3 Striking a balance between trade-offs
    3. 2.3 Expert knowledge: Deep domain understanding
      1. 2.3.1 Clarifying business context of opportunities
      2. 2.3.2 Accounting for domain data source nuances
      3. 2.3.3 Navigating organizational structure
    4. 2.4 Self-assessment and development focus
      1. 2.4.1 Understanding your interests and leadership strengths
      2. 2.4.2 Practicing with the CPR process
      3. 2.4.3 Developing a prioritize, practice, and perform plan
      4. 2.4.4 Note for DS tech lead managers
    5. Summary
    6. References
  11. 3 Virtues for leading projects
    1. 3.1 Ethical standards of conduct
      1. 3.1.1 Operating in the customers’ best interest
      2. 3.1.2 Adapting to business priorities in dynamic business environments
      3. 3.1.3 Imparting knowledge confidently
    2. 3.2 Rigor cultivation, higher standards
      1. 3.2.1 Getting clarity on the fundamentals of scientific rigor
      2. 3.2.2 Monitoring for anomalies in data and in deployment
      3. 3.2.3 Taking responsibility for enterprise value
    3. 3.3 Attitude of positivity
      1. 3.3.1 Exhibiting positivity and tenacity to work through failures
      2. 3.3.2 Being curious and collaborative in responding to incidents
      3. 3.3.3 Respecting diverse perspectives in lateral collaborations
    4. 3.4 Self-assessment and development focus
      1. 3.4.1 Understanding your interests and leadership strengths
      2. 3.4.2 Practicing with the CPR process
      3. 3.4.3 Self-coaching with the GROW model
      4. 3.4.4 Note for DS tech lead managers
    5. Summary
    6. References
  12. Part 2. The manager: Nurturing a team
    1. Reference
  13. 4 Capabilities for leading people
    1. 4.1 Technology: Tools and skills
      1. 4.1.1 Delegating projects effectively
      2. 4.1.2 Managing for consistency across models and projects
      3. 4.1.3 Making build-versus-buy recommendations
    2. 4.2 Execution: Best practices
      1. 4.2.1 Building powerful teams under your supervision
      2. 4.2.2 Influencing partner teams to increase impact
      3. 4.2.3 Managing up to your manager
    3. 4.3 Expert knowledge: Deep domain understanding
      1. 4.3.1 Broadening knowledge to multiple technical and business domains
      2. 4.3.2 Understanding the fundamental domain opportunities
      3. 4.3.3 Assessing ROI for prioritization, despite missing data
    4. 4.4 Self-assessment and development focus
      1. 4.4.1 Understanding your interests and leadership strengths
      2. 4.4.2 Practicing with the CPR process
    5. Summary
    6. References
  14. 5 Virtues for leading people
    1. 5.1 Ethical standards of conduct
      1. 5.1.1 Growing the team with coaching, mentoring, and advising
      2. 5.1.2 Representing the team confidently in cross-functional discussions
      3. 5.1.3 Contributing to and reciprocating on broader management duties
    2. 5.2 Rigor nurturing, higher standards
      1. 5.2.1 Observing and mitigating anti-patterns in ML and DS systems
      2. 5.2.2 Learning effectively from incidents
      3. 5.2.3 Driving clarity by distilling complex issues into concise narratives
    3. 5.3 Attitude of positivity
      1. 5.3.1 Managing the maker’s schedule versus the manager’s schedule
      2. 5.3.2 Trusting the team members to execute
      3. 5.3.3 Creating a culture of institutionalized learning
    4. 5.4 Self-assessment and development focus
      1. 5.4.1 Understanding your interests and leadership strengths
      2. 5.4.2 Practicing with the CPR process
    5. Summary
    6. References
  15. Part 3. The director: Governing a function
  16. 6 Capabilities for leading a function
    1. 6.1 Technology: Tools and skills
      1. 6.1.1 Crafting technology roadmaps
      2. 6.1.2 Guiding the DS function to build the right features for the right people at the right time
      3. 6.1.3 Sponsoring and championing promising projects
    2. 6.2 Execution: Best practices
      1. 6.2.1 Delivering consistently by managing people, processes, and platforms
      2. 6.2.2 Building a strong function with clear career maps and a robust hiring process
      3. 6.2.3 Supporting executives in top company initiatives
    3. 6.3 Expert knowledge: Deep domain understanding
      1. 6.3.1 Anticipating business needs across stages of product development
      2. 6.3.2 Applying initial solutions rapidly to urgent issues
      3. 6.3.3 Driving fundamental impacts with deep domain understanding
    4. 6.4 Self-assessment and development focus
      1. 6.4.1 Understanding your interests and leadership strengths
      2. 6.4.2 Practicing with the CPR process
    5. Summary
    6. References
  17. 7 Virtues for leading a function
    1. 7.1 Ethical standards of conduct
      1. 7.1.1 Establishing project formalizations across the function
      2. 7.1.2 Coaching as a social leader with interpretations, narratives, and requests
      3. 7.1.3 Organizing initiatives to provide career growth opportunities
    2. 7.2 Rigor in planning, higher standards
      1. 7.2.1 Driving a successful annual planning process
      2. 7.2.2 Avoiding project planning and execution anti-patterns
      3. 7.2.3 Securing commitments from partners and teams
    3. 7.3 Attitude of positivity
      1. 7.3.1 Recognizing and promoting diversity within your team
      2. 7.3.2 Practicing inclusion in decision-making
      3. 7.3.3 Nurture belonging to your function
    4. 7.4 Self-assessment and development focus
      1. 7.4.1 Understanding your interests and leadership strengths
      2. 7.4.2 Practicing with the CPR process
    5. Summary
    6. References
  18. Part 4. The executive: Inspiring an industry
  19. 8 Capabilities for leading a company
    1. 8.1 Technology: Tools and skills
      1. 8.1.1 Architecting one- to three-year business strategies and roadmaps in data
      2. 8.1.2 Delivering data-driven culture in all aspects of business processes
      3. 8.1.3 Structuring innovative and productive data science organizations
    2. 8.2 Execution: Best practices
      1. 8.2.1 Infusing data science capabilities into the vision and mission
      2. 8.2.2 Building a strong talent pool in data science
      3. 8.2.3 Clarifying your role as composer or conductor
    3. 8.3 Expert knowledge: Deep domain understanding
      1. 8.3.1 Identifying differentiation and competitiveness among industry peers
      2. 8.3.2 Guiding business through pivots when required
      3. 8.3.3 Articulating business plans for new products and services
    4. 8.4 Self-assessment and development focus
      1. 8.4.1 Understanding your interests and leadership strengths
      2. 8.4.2 Practicing with the CPR process
    5. Summary
    6. References
  20. 9 Virtues for leading a company
    1. 9.1 Ethical standards of conduct
      1. 9.1.1 Practicing responsible machine learning based on ethical principles
      2. 9.1.2 Ensuring the trust and safety of customers
      3. 9.1.3 Taking social responsibility for decisions
    2. 9.2 Rigor in leading, higher standards
      1. 9.2.1 Creating a productive and harmonious work environment
      2. 9.2.2 Accelerating the speed and increasing the quality of decisions
      3. 9.2.3 Focusing on increasing enterprise value
    3. 9.3 Attitude of positivity
      1. 9.3.1 Demonstrating executive presence
      2. 9.3.2 Establishing team identity of industry leadership
      3. 9.3.3 Learning and adopting best practices across different industries
    4. 9.4 Self-assessment and development focus
      1. 9.4.1 Understanding your interests and leadership strengths
      2. 9.4.2 Practicing with the CPR process
    5. Summary
    6. References
  21. Part 5. The LOOP and the future
  22. 10 Landscape, organization, opportunity, and practice
    1. 10.1 The landscape
      1. 10.1.1 Data lakehouse
      2. 10.1.2 Stream processing
      3. 10.1.3 Self-serve insight
      4. 10.1.4 Data and ML operations automation
      5. 10.1.5 Data governance
      6. 10.1.6 Periodic review for major architecture trends
    2. 10.2 The organization
      1. 10.2.1 Functional organizational structure
      2. 10.2.2 Divisional organizational structure
      3. 10.2.3 Matrix organizational structure
      4. 10.2.4 Alternative organizational structure
      5. 10.2.5 Managing for opportunities and challenges in various structures
    3. 10.3 The opportunity
      1. 10.3.1 Assessing an industry
      2. 10.3.2 Assessing a company
      3. 10.3.3 Assessing the team
      4. 10.3.4 Assessing the role
      5. 10.3.5 Onboarding into a new role
    4. 10.4 The practice
      1. 10.4.1 Skill sets you can hire into your team
      2. 10.4.2 Emerging career directions for DS leaders
    5. 10.5 Reviewing the LOOP
    6. Summary
    7. References
  23. 11 Leading in data science and a future outlook
    1. 11.1 The why, what, and how of leading in DS
      1. 11.1.1 Why is learning to lead in DS increasingly important?
      2. 11.1.2 What is a framework for leading in DS?
      3. 11.1.3 How to use the framework in practice?
    2. 11.2 The future outlook
      1. 11.2.1 The role: The emergence of data product managers
      2. 11.2.2 The capability: The availability of function-specific data solutions
      3. 11.2.3 The responsibility: Instilling trust in data
    3. Summary
    4. References
  24. epilogue
  25. index
  26. inside back cover

Product information

  • Title: How to Lead in Data Science
  • Author(s): Jike Chong, Yue Cathy Chang
  • Release date: December 2021
  • Publisher(s): Manning Publications
  • ISBN: 9781617298899