Who Are The Real Data Science Unicorns: Generalists Vs Specialists

What is a unicorn Data Scientist?

Just to start off in a very generalized yet understandable way:

“A mythical beast with magical powers who’s rumored to exist but is never actually seen in the wild.”

The data science unicorn is a somewhat mythical person who is a leader in data science, technology, and business. Of course, these candidates practically don’t exist, nor do they necessarily make strong team members. As data science teams have grown, businesses have moved away from trying to find that one person to fill different roles; instead, companies have realized the benefits of hiring employees with specialized, complementary skills.

A Specialist or a Generalist?

The Data Science community is getting bigger and bigger at the highest rate in the world right now and so are the needs, and now the modern data scientists have 2 options to progress their career, being a specialist or a generalist.

         *Specialists – go narrow and deep and really specialize on one particular area such as machine                                             learning or data engineering.

         *Generalist – go broad and shallow and cover off more of the general bases.

 

In the Data Science community, specialists are heavily favored over generalists — that’s just the way it is. We inherently believe that more specialization is a sure-fire way to success in a role or for a business outcome. Actually, That shouldn’t be the case.

While specialists are excellent at reproducing work that they are well-practiced at, sometimes they struggle to navigate uncharted territory where rules are not well defined. Ironically, many business roles, especially ones at small and nimble companies, require just such a skill ,the ability to deal with ambiguity, uncertainty, and lack of clear rules.

The “specialist” model, the organization will hire an individual or set of individuals that can perform the tasks set out in each segment of development. 

This can work in large organizations, typically large companies have the resources to hire hyper-specialists effectively for every phase of development, as well as hire and/or place leadership that oversees the entire process.

That theory gets flipped on its head if you are running a smaller organisation, like a small company or early stage startup. With a small (or non-existent) data team, you’re much more likely to look for new hires with skill-sets that cover more of the bases at once. The luxury of hiring someone who “only” does machine learning engineering just isn’t available to you.

The “generalist” model does not place unneeded emphasis on specialization, but rather seeks to properly connect the nature of the problem being hired for, with the unique set of experiences of each candidate.

The Best fit

It is a long standing debate. Let us say, you want to hire a data scientist for your new retail startup and you have 2 potential candidates:

Candidate I: 8 Years of experience working in the Retail industry as a machine learning engineer worked on recommendation systems, speech recognitioncomputer vision, constantly evaluating various ML models and deploying them to production.

Candidate II: 3 Years of experience in the Finance industry as a data scientist worked on recommendation systems, speech recognition with a shallow but broad skill set of data science including big data platforms, data cleaning, building models, and deployment.

 

Whom to hire? 

The answer depends on what type of problems you are hiring a data scientist for, if you are looking for a person who can provide business intelligence then domain knowledge is highly preferred.

Or if you want to set up the data science team for your company. First, you need someone that can help create the data architecture, pipelines, and systems so that you’ve got data sets that you can even begin to look at. Then you can start thinking about simple “if-then” scenarios that will lead, eventually, to “AI.” Start with the generalist that can lay the basic groundwork for a program, then later, probably much later, hire specialists that can add depth to the individual components when (and only when) the business demands it.

At the end of the day, they all had been doing and have to work on structured or unstructured data, and switching industries won’t bring much trouble to the miners.

Does it matter?

If we are talking about specialization, It does. However, lend itself to working in very large corporate companies. They usually have the luxury of well resourced data science departments with very specific tasks for each team. If you have a data engineering team, an analyst team, and a modelling team, it makes more sense to look for new hires that fit into one of those specific pigeon holes.

To end with, Randy Au has a great quote in his article Succeeding as a data scientist in small companies/startups which sums up the “not quite knowing” what you’ll need the data team to do in your small company:

It’s usually quite likely that they don’t really have a full understanding of what they need. There’s just a generalized sense of “we have data, it seems useful, but we don’t have anyone who has the skills to make it useful.”

Further he quote: 

“The main thing is being able to roll up your sleeves and make a difference on the ground TODAY. Then make sure that you’re building the right infrastructure to make full use of data/analytics/data science there TOMORROW. A machine learning engineer, regardless of their talent and smarts, just won’t be the right person for that particular job. You NEED a generalist.”

 

 

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