in bulk directly from the pages on a website, in other words) for fresh data in order to discover and report the latest breaking-news stories. (I talk more about data storytelling in Chapter 8.)
Directors of data science bolster their technical project management capabilities with an added expertise in data science. Their work includes leading data projects and working to protect the profitability of the data projects for which they’re responsible. They also act to ensure transparent communication between C-suite executives, business managers, and the data personnel on their team who actually do the implementation work. (I share more details in Part 4 about leading successful data projects; check out Chapter 18 for details about data science leaders.)
Data product managers supercharge their product management capabilities with the power of data science. They use data science to generate predictive insights that better inform decision-making around product design, development, launch, and strategy. This is a classic type of data leadership role, the likes of which are covered in Chapter 18. For more on developing effective data strategy, take a gander at Chapters 15 through 17.
Machine learning engineers combine software engineering superpowers with data science skills to build predictive applications. This is a classic data implementation role, more of which is discussed in Chapter 2.
Communicating data insights
As a data scientist, you must have sharp verbal communication skills. If a data scientist can’t communicate, all the knowledge and insight in the world does nothing for the organization. Data scientists need to be able to explain data insights in a way that staff members can understand. Not only that, data scientists need to be able to produce clear and meaningful data visualizations and written narratives. Most of the time, people need to see a concept for themselves in order to truly understand it. Data scientists must be creative and pragmatic in their means and methods of communication. (I cover the topics of data visualization and data-driven storytelling in much greater detail in Chapter 8.)
Exploring Career Alternatives That Involve Data Science
Not to cause alarm, but it’s fully possible for you to develop deep and sophisticated data science skills and then come away with a gut feeling that you know you’re meant to do something more.
Earlier in my data career, I was no stranger to this feeling. I’d just gone and pumped up my data science skills. It was the “sexiest” career path — according to Harvard Business Review in 2012 — and offered so many opportunities. The money was good and the demand was there. What’s not to love about opportunities with big tech giants, start-ups, and multiple six-figure salaries, right?
But very quickly, I realized that, although I had the data skills and education I needed to land some sweet opportunities (including interview offers from Facebook!), I soon realized that coding away and working only on data implementation simply weren’t what I was meant to do for the rest of my life.
Something about getting lost in the details felt disempowering to me. My personality craved more energy, more creativity — plus, I needed to see the big-picture impact that my data work was making.
In short, I hadn’t yet discovered my inner data superhero. I coined this term to describe that juicy combination of a person’s data skills, coupled with their personality, passions, goals, and priorities. When all these aspects are in sync, you’ll find that you’re absolutely on fire in your data career. These days, I’m a data entrepreneur. I get to spend my days doing work that I absolutely adore and that’s truly aligned with my mission and vision for my data career and life-at-large. I want the same thing for you, dear reader.
https://businessgrowth.ai/), you can find free access to a fun, 45-second quiz about data career paths. It helps you uncover your own inner data superhero type. Take the quiz to receive personalized data career recommendations that directly align with your unique combination of data skills, personality, and passions.
For now, let’s take a look at the three main data superhero archetypes that I’ve seen evolving and developing over the past decade.
The data implementer
Some data science professionals were simply born to be implementers. If that’s you, then your secret superpower is building data and artificial intelligence (AI) solutions. You have a meticulous attention to detail that naturally helps you in coding up innovative solutions that deliver reliable and accurate results — almost every time. When you’re facing a technical challenge, you can be more than a little stubborn. You’re able to accomplish the task, no matter how complex.
Without implementers, none of today’s groundbreaking technologies would even exist. Their unparalleled discipline and inquisitiveness keep them in the problem-solving game all the way until project completion. They usually start off a project with a simple request and some messy data, but through sheer perseverance and brainpower, they're able to turn them into clear and accurate predictive data insights — or a data system, if they prefer to implement data engineering rather than data science tasks. If you’re a data implementer, math and coding are your bread-and-butter, so to speak.
Part 2 of this book are dedicated to showing you the basics of data science and the skills you need to take on to get started in a career in data science implementation. You may also be interested in how your work in this area is applied to improve a business’s profitability. You can read all about this topic in Part 3.
The data leader
Other data science professionals naturally gravitate more toward business, strategy, and product. They take their data science expertise and apply it to lead profit-forming data science projects and products. If you’re a natural data leader, then you’re gifted at leading teams and project stakeholders through the process of building successful data solutions. You’re a meticulous planner and organizer, which empowers you to show up at the right place and the right time, and hopefully keep your team members moving forward without delay.
Data leaders love data science just as much as data implementers and data entrepreneurs — you can read about them in the later section “The data entrepreneur.” The difference between most data implementers and data leaders is that leaders generally love data science for the incredible outcomes that it makes possible. They have a deep passion for using their data science expertise and leadership skills to create tangible results. Data leaders love to collaborate with smart people across the company to get the job done right. With teamwork, and some input from the data implementation team, they form brilliant plans for accomplishing any task, no matter how complex. They harness manpower, data science savvy, and serious business acumen to produce some of the most innovative technologies
