Six Core Competencies Data Scientists Need to Succeed in Their Careers
Editor’s note: David Stephenson is a speaker for ODSC Europe this June 14th-15th. Be sure to check out his talk, “Equipping your analytics professions with the most critical business skills,” there!
Being a data scientist, as it turns out, is a lot more complicated than you and I had realized back when we were first taking courses in machine learning and programming.
But when we landed our first jobs, we quickly realized that it’s not actually the algorithms or the coding that are so difficult. Instead, it’s the non-technical challenges that make our lives so difficult — challenges such as managing stakeholder expectations, delivering clear presentations, and navigating office politics.
Although I’ve managed, trained, and worked alongside literally hundreds of data scientists over the years, I don’t remember ever hearing someone complain that the technical work was too difficult. But I have often listened to them describe other struggles related to the business — being assigned mind-numbingly boring tasks, aiming at moving targets, or struggling with unclear expectations.
On the flip side, I’ve also listened to executives complain about how their data scientists are not effectively communicating, managing projects, relating to stakeholders, or building trust. And they’ve lamented the difficulty of hiring a data scientist who can produce business value and work well with colleagues.
And so, like it or not, we’ve learned that business skills can make or break our careers as analytic professionals.
A few years ago, some of my consulting partners asked me to train up-and-coming data scientists in the soft skills they would need. We thought for a while and narrowed it down to skills falling within six competencies: company, colleagues, communication (storytelling), expectations, results, and career.
The first competency, company, is about getting to the point where our non-technical colleagues recognize us as valuable contributors. It’s about them finding our place within an organization, understanding what others expect from us, and learning how to produce real value. It’s also about understanding how different stakeholders can expect completely different things.
The second competency, colleagues, is about interpersonal challenges that often strike us out of nowhere — cultural misunderstandings, disagreements, and office politics. Younger data scientists generally perceive these topics as less important, until the day when they suddenly find them to be extremely important.
The third competency, storytelling, is about choosing and communicating our messages. It’s not only about making good graphs and tables, but also about really impactful messaging. There are some easy tricks with quick wins in this area, but it’s surprising how few data scientists understand them.
The fourth competency, expectations, is for more senior data scientists who need to scope and manage projects while maintaining stakeholder trust. For example, it’s critical to build consensus at the start of a project and to avoid the common mistake of working hard to build a solution that no one will use. And it’s especially challenging, but absolutely critical, to keep two-way communication going throughout the project.
The fifth competency, results, includes mastering principles and frameworks for selecting the most beneficial data science projects as well as best practices for running such projects to successful completion. The shifting expectations and lack of clarity that are so prevalent in data science projects makes this especially challenging.
The sixth and final competency, career, is related to questions I often get asked by data scientists, such as how to choose between job opportunities, how to strengthen your CV, and even whether to go freelance.
Although I spend most of my own time doing hands-on project leadership for clients, I have also continued to develop and deliver these business skill trainings over the past few years. When Covid lockdowns hit, I decided it was time to finally share the key concepts with a broader audience. The resulting book, “Business Skills for Data Scientists: Practical Guidance in Six Key Topics”, was published in 2021 and has so far had a very positive reception.
In this ODSC talk, I’ll explain the core business skills covered in my book, illustrate why each is so critical for analytics professionals, and point out ways in which leaders can foster these skills within their analytics teams. Along the way, I’ll draw on best practices, case studies, research, and personal stories from my 20+ years of experience in the field.
About the author/ODSC Europe 2023 speaker:
David Stephenson has over 20 years of experience leading analytics initiatives, including as Head of Global Business Analytics at eBay Classifieds Group. Since founding DSI Analytics in 2014, he has worked directly with dozens of companies across a wide range of industries (Adidas, Miro, Janssen Pharmaceuticals, ABN Amro, Sky Broadcasting, etc). Dr. Stephenson also serves as part time faculty at the University of Amsterdam Business School, has published two books, and has developed and delivered data science trainings for hundreds of analytics professionals around the globe.
Originally posted on OpenDataScience.com
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