How to Lead in Data Science

by Jike Chong, Yue Cathy Chang

Overview

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

"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

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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 author
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.

Table of Contents
1 What makes a successful data scientist?
PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP
2 Capabilities for leading projects
3 Virtues for leading projects
PART 2 THE MANAGER: NURTURING A TEAM
4 Capabilities for leading people
5 Virtues for leading people
PART 3 THE DIRECTOR: GOVERNING A FUNCTION
6 Capabilities for leading a function
7 Virtues for leading a function
PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY
8 Capabilities for leading a company
9 Virtues for leading a company
PART 5 THE LOOP AND THE FUTURE
10 Landscape, organization, opportunity, and practice
11 Leading in data science and a future outlook

Product Details

ISBN-13:	9781617298899
Publisher:	Manning
Publication date:	12/21/2021
Pages:	512
Product dimensions:	7.38(w) x 9.25(h) x 1.10(d)

About the Author

Dr. Jike Chong and Yue Cathy Chang have both built, led, and grown multiple high-performing data teams. Dr. Chong developed the Yiren Digital Ltd. data team from the ground up, expanded and led the data team as chief data scientist at Acorns, and recently led the Hiring Marketplace Data Science team at LinkedIn.


Yue Cathy Chang and her team "parachuted" into companies to address challenging and meaningful data needs. She has worked with leaders of centralized and distributed data teams at organizations big and small, including large asset-management firms and Fortune 50 companies.

Table of Contents

Foreword xv

Preface xvii

Acknowledgments xx

About this book xxii

About the authors xxvii

About the cover illustration xxix

1 What makes a successful data scientist? 1

1.1 Data scientist expectations 2

The Venn diagram a decade later 3

What is missing? 3

Understanding ability and motivation: Assessing capabilities and virtues 5

1.2 Career progression in data science 6

Interview and promotion woes 7

What are (hiring) managers looking for? 11

Part 1 The tech lead: Cultivating leadership 21

2 Capabilities for leading projects 23

2.1 Technology: Tools and skills 24

Framing the problem to maximize business impact 25

Discovering patterns in data 26

Setting expectations for success 36

2.2 Execution: Best practices 40

Specifying and prioritizing projects from vague requirements 40

Planning and managing data science projects 44

Striking a balance between trade-offs 51

2.3 Expert knowledge: Deep domain understanding 56

Clarifying business context of opportunities 57

Accounting for domain data source nuances 58

Navigating organizational structure 63

2.4 Self-assessment and development focus 66

Understanding your interests and leadership strengths 66

Practicing with the CPR process 68

Developing a prioritize, practice, and perform, plan 69

Note for DS tech lead managers 69

3 Virtues for leading projects 72

3.1 Ethical standards of conduct 73

Operating in the customers' best interest 74

Adapting to business priorities in dynamic business environments 75

Imparting knowledge confidently 80

3.2 Rigor cultivation, higher standards 83

Getting clarity on the fundamentals of scientific rigor 84

Monitoring for anomalies in data and in deployment 88

Taking responsibility for enterprise value 92

3.3 Attitude of positivity 104

Exhibiting positivity and tenacity to work through failures 104

Being curious and collaborative in responding to incidents 105

Respecting diverse perspectives in lateral collaborations 108

3.4 Self-assessment and development focus 110

Understanding your interests and leadership strengths 111

Practicing with the CPR process 112

Self-coaching with the GROW model 112

Note for DS tech lead managers 114

Part 2 The manager: Nurturing a team 117

4 Capabilities for leading people 119

4.1 Technology: Tools and skills 120

Delegating projects effectively 120

Managing for consistency across models and projects 123

Making build-versus-buy recommendations 126

4.2 Execution: Best practices 129

Building powerful teams under your supervision 129

Influencing partner teams to increase impact 139

Managing up to your manager 142

4.3 Expert knowledge: Deep domain understanding 145

Broadening knowledge to multiple technical and business domains 146

Understanding the fundamental domain opportunities 149

Assessing ROI for prioritization, despite missing data 152

4.4 Self-assessment and development focus 155

Understanding your interests and leadership strengths 155

Practicing with the CPR process 157

5 Virtues for leading people 159

5.1 Ethical standards of conduct 160

Growing the team with coaching, mentoring, and advising 160

Representing the team confidently in cross-functional discussions 164

Contributing to and reciprocating on broader management duties 168

5.2 Rigor nurturing, higher standards 171

Observing and mitigating anti-patterns in ML and DS systems 171

Learning effectively from incidents 176

Driving clarity by distilling complex issues into concise narratives 179

5.3 Attitude of positivity 182

Managing the maker's schedule versus the manager's schedule 183

Trusting the team members to execute 187

Creating a culture of institutionalized learning 192

5.4 Self-assessment and development focus 194

Understanding your interests and leadership strengths 194

Practicing with the CPR process 196

Part 3 The director: Governing a function 199

6 Capabilities for leading a function 201

6.1 Technology: Tools and skills 202

Crafting technology roadmaps 203

Guiding the DS function to build the right features for the right people at the right time 210

Sponsoring and championing promising projects 213

6.2 Execution: Best practices 218

Delivering consistently by managing people, processes, and platforms 219

Building a strong function with clear career maps and a robust hiring process 229

Supporting executives in top company initiatives 237

6.3 Expert knowledge: Deep domain understanding 241

Anticipating business needs across stages of product development 241

Applying initial solutions rapidly to urgent issues 245

Driving fundamental impacts with deep domain understanding 248

6.4 Self-assessment and development focus 253

Understanding your interests and leadership strengths 253

Practicing with the CPR process 254

7 Virtues for leading a Junction 257

7.1 Ethical standards of conduct 258

Establishing project formalizations across the function 258

Coaching as a social leader with interpretations, narratives, and requests 262

Organizing initiatives to provide career growth opportunities 266

7.2 Rigor in planning, higher standards 271

Driving a successful annual planning process 272

Avoiding project planning and execution anti-patterns 276

Securing commitments from partners and teams 280

7.3 Attitude of positivity 284

Recognizing and promoting diversity within your team 285

Practicing inclusion in decision-making 289

Nurture belonging to your function 292

7.4 Self-assessment and development focus 294

Understanding your interests and leadership strengths 295

Practicing with the CPR process 296

Part 4 The executive Inspiring an industry 299

8 Capabilities for leading a company 301

8.1 Technology: Tools and skills 302

Architecting one- to three-year business strategies and roadmaps in data 302

Delivering data-driven culture in all aspects of business processes 307

Structuring innovative and productive data science organizations 314

8.2 Execution: Best practices 318

Infusing data science capabilities into the vision and mission 319

Building a strong talent pool in data science 323

Clarifying your role as composer or conductor 327

8.3 Expert knowledge: Deep domain understanding 330

Identifying differentiation and competitiveness among industry peers 330

Guiding business through pivots when required 334

Articulating business plans for new products and services 337

8.4 Self-assessment and development focus 340

Understanding your interests and leadership strengths 340

Practicing with the CPR process 342

9 Virtues for leading a company 345

9.1 Ethical standards of conduct 346

Practicing responsible machine learning based on ethical principles 346

Ensuring the trust and safety of customers 350

Taking social responsibility for decisions 354

9.2 Rigor in leading, higher standards 357

Creating a productive and harmonious work environment 358

Accelerating the speed and increasing the quality of decisions 362

Focusing on increasing enterprise value 365

9.3 Attitude of positivity 372

Demonstrating executive presence 372

Establishing team identity of industry leadership 378

Learning and adopting best practices across different industries 382

9.4 Self-assessment and development focus 386

Understanding your interests and leadership strengths 386

Practicing with the CPR process 387

Part 5 The Loop and the future 391

10 Landscape, organization, opportunity, and practice 393

10.1 The landscape 394

Data lakehouse 395

Stream processing 396

Self serve insight 397

Data and ML operations automation 398

Data governance 399

Periodic review for major architecture trends 401

10.2 The organization 401

Functional organizational structure 402

Divisional organizational structure 405

Matrix organizational structure 406

Alternative organizational structure 409

Managing for opportunities and challenges in various structures 410

10.3 The opportunity 411

Assessing an industry 412

Assessing a company 413

Assessing the team 416

Assessing the role 419

Onboarding into a new role 420

10.4 The practice 430

Skill sets you can hire into your team 430

Emerging career directions for DS leaders 435

10.5 Reviewing the LOOP 439

11 Leading in data science and a future outlook 444

11.1 The why, what, and how of leading in DS 445

Why is learning to lead in DS increasingly important? 445

What is a framework for leading in DS? 447

How to use the framework in practice! 452

11.2 The future outlook 453

The role: The emergence of data product managers 453

The capability: The availability of function-specific data solutions 457

The responsibility: Instilling trust in data 461

Epilogue 465

Index 467