Why You Should be a Data Science Generalist - and How to Become One

The new advice today for data scientists is not to become a generalist. You can read recent articles on this topic, for instance here.  In this blog, I explain why I believe it should be the opposite. I wrote about this here not long ago, and provide additional arguments in this article, as to why it helps to be a generalist.  

Of course, it is difficult, and probably impossible to become a data science generalist just after graduating. It takes years to acquire all the skills, yet you don't need to master all of them. It might be easier for a physicist, engineer, or biostatistician currently learning data science, after years of corporate experience, than it is for a data scientist with no business experience. Possibly the easiest way to become one is to work for start-up's or small companies, taking on many hats as you will probably be the only data scientist in your company, and will have to change jobs more frequently than if you work for a big company. To the contrary, for a big company, you are expected to work in a very specialized area, though it does not hurt to be a generalist, as I will illustrate shortly. Being a specialized data scientist could put you on a very predictable path that limits your career growth and flexibility, especially if you want to create your company down the line. Let's start with explaining what a data science generalist is.

The data science generalist

The generalist has experience working in different roles and different environments, for instance, over a period of 15 years, having worked as a

• Business analyst or BI professional, communicating insights to decision makers, mastering tools such as Tableau, SQL and Excel; or maybe being the decision maker herself
• Statistician / data analyst with expertise in predictive modeling
• Expert in algorithm design and optimization
• Researcher in an academic-like setting, or experience in testing / prototyping new data science systems and proofs of concept (POC)
• Builder / architect: designing APIs, dashboards, databases, and deploying/maintaining yourself some modest systems in production mode
• Programmer (statistical or scientific programmer with exposure to high performance computing and parallel architectures - you might even have designed your own software)
• Consultant, directly working with clients, or adviser
• Manager or director role rather than individual contributor
• Professional with roles in various industries (IT, media, Internet, finance, health care, smart cities) in both big and small companies, in various domains ranging from fraud detection, to optimizing sales or marketing, with proven, measurable accomplishments

In short, the generalist has been involved at one time or another, in all phases of the data science project lifecycle. 

The generalist might not command a higher salary, but has more flexibility career-wise. Even in a big company, when downsizing occurs, it is easier for the generalist to make a lateral move (get transferred to a different department), than it is for the "one-trick pony". 

Timing is important too. If you become a generalist at age 50 (as opposed to age 45) it might not help as getting hired becomes more difficult as you get past 45. Still, even if 50 or more, it opens up some possibilities, for instance starting your own business. And if you can prove that you have been consistently broadening your skills throughout your career cycle, as generalists do by definition, it will be easier to land a job, especially if your salary expectations are reasonable, and your health is not an issue for your future employer.  

To read the full article, click here.