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Specialists vs. Generalists: Professionally, How Do They Compare?

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Specialists vs. Generalists: Professionally, How Do They Compare?

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When it comes to determining valuable assets at work, employees come to mind first. And then we have to ask, who would bring in success — the person who knows how to wear many hats well, or the person who has mastered a specific skill? Web design and development is one such niche that demands complete dedication and in-depth knowledge of the latest tools and practices. There are pros and cons to both specialists and generalists; the deciding factor is how their skills are utilized.

Specialists: pros and cons

A specialist has chosen an area to specialize in after thorough evaluation and study. Not only do they have enough experience to produce desired deliverables, but they are also well-equipped to handle future changes, should they arise. Specialists focus on a single area, which can come with many pros.

For instance, specialist web designers know their platform well and can produce the required outcomes. By default, they have the know-how necessary to deal with any hiccups along the way when it comes to a particular area of expertise. And from a business perspective, a specialist web designer, developer, or quality analyst generally seems more marketable and has greater success selling their services.

However, from an organizational perspective, hiring a specialist is not always the ideal solution by default. At times, the work may slow down, or the work in a particular technology may taper off. This is an instance in which a specialist may feel the need to draw on other skills, but would not have the variety a generalist does.

Not every organization would prefer to hire a specialist web developer or database manager, if only because the market is too narrow to provide easy compensation. With many specialties at risk of becoming obsolete due to the arrival of new ones, a specialist could potentially be seen as less valuable if other skills end up being required for a job.

Generalists: pros and cons

Typically, generalists enjoy better chances of success than specialists because they always have a variety of services to offer. And with new innovations in IT, many organizations prefer to hire people who can do more than one thing (and do them with precision). This tends to cut costs and ensure that certain processes will be less time-consuming or cumbersome.

A generalist web developer has a wide array of skills — everything from developing a site to performing a quality check and even coming up with a design template. Keeping this in mind, a generalist may be an easy sell because their variety of skills don’t always come with the higher price tag of someone who specializes in one specific area.

The internet has become too complex for someone with limited skills to master, which tends to put generalists in a better position nowadays. An important thing to consider when deciding between a specialist and a generalist is that the latter should not be mistaken for someone who lacks skills; in reality, they have skills that delve into most web-based needs today. Meanwhile, specialists will always have the upper hand when it comes to getting the job done in a highly specific area. What matters most is what your organization is looking for and which of the two can meet your needs.

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KDNuggests | Unicorn Data Scientists vs Data Science Teams

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KDNuggests | Unicorn Data Scientists vs Data Science Teams

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I think a realistic goal to shoot for is (1) a set of core skills, (2) deep expertise in 1 or more focus areas with some basic competency in the rest, and (3) some amount of domain expertise acquired over time through applied work.

These might look something like this:

Core Skills

Basic CS, Software Development, Tools

Data Engineering (Distributed Computing, etc.)

Scientific Training, Mathematics, Modeling, Theory

Focus Areas

Machine Learning

Business Analytics

Graph Mining / Network Intelligence

Text Mining / Information Retrieval

Data Visualization

etc.

Domain Expertise

Finance

Consumer Internet

Oil & Gas

Bioinformatics

Physics

Advertising

etc.

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Domain Expertise is one of the de facto definitions of Data Science, so I think you can start off being a generalist but will ultimately be known as being a specialist in a certain industry or industries (don't spread yourself thin!). You can either carve out your own niche, or the market will end up defining you.  So concentrate on the industry that you like. Ultimately, companies like to hire people who have expertise in their industry, regardless of their job description.  Even for consultants.

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Data Scientists: Generalists or specialists?

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Data Scientists: Generalists or specialists?

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Editor's note: This is the second in a three-part series of posts by Daniel Tunkelang dedicated to data science as a profession. In this series, Tunkelang will cover the recruiting, organization, and essential functions of data science teams.

When LinkedIn posted its first job opening for a "data scientist" in 2008, the company was clearly looking for generalists:

Be challenged at LinkedIn. We’re looking for superb analytical minds of all levels to expand our small team that will build some of the most innovative products at LinkedIn.

No specific technical skills are required (we’ll help you learn SQL, Python, and R). You should be extremely intelligent, have quantitative background, and be able to learn quickly and work independently. This is the perfect job for someone who’s really smart, driven, and extremely skilled at creatively solving problems. You’ll learn statistics, data mining, programming, and product design, but you’ve gotta start with what we can’t teach—intellectual sharpness and creativity.

In contrast, most of today's data scientist jobs require highly specific skills. Some employers require knowledge of a particular programming language or tool set. Others expect a Ph.D. and significant academic background in machine learning and statistics. And many employers prefer candidates with relevant domain experience.

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If you are building a team of data scientists, should you hire generalists or specialists? As with most things, it depends. Consider the kinds of problems your company needs to solve, the size of your team, and your access to talent. But, most importantly, consider your company's stage of maturity.

Early days

Generalists add more value than specialists in a company’s early days, since you’re building most of your product from scratch and something is better than nothing. Your first classifier doesn't have to use deep learning to achieve game-changing results. Nor does your first recommender system need to use gradient-boosted decision trees. And a simple t-test will probably serve your A/B testing needs.

Hence, the person building the product doesn't need to have a Ph.D. in statistics or 10 years of experience working with machine learning algorithms. What's more useful in the early days is someone who can climb around the stack like a monkey and do whatever needs doing, whether it’s cleaning data or native mobile app development.

How do you identify a good generalist? Ideally this is someone who has already worked with data sets that are large enough to have tested his or her skills regarding computation, quality, and heterogeneity. Surely someone with a STEM background, whether through academic or on-the-job training, would be a good candidate. And someone who has demonstrated the ability and willingness to learn how to use tools and apply them appropriately would definitely get my attention. When I evaluate generalists, I ask them to walk me through projects that showcase their breadth.

Later stage

Generalists hit a wall as your products mature: they’re great at developing the first version of a data product, but they don’t necessarily know how to improve it. In contrast, machine learning specialists can replace naive algorithms with better ones and continuously tune their systems. At this stage in a company’s growth, specialists help you squeeze additional opportunity from existing systems. If you're a Google or Amazon, those incremental improvements represent phenomenal value.

Similarly, having statistical expertise on staff becomes critical when you are running thousands of simultaneous experiments and worrying about interactions, novelty effects, and attribution. These are first-world problems, but they are precisely the kinds of problems that call for senior statisticians.

How do you identify a good specialist? Look for someone with deep experience in a particular area, like machine learning or experimentation. Not all specialists have advanced degrees, but a relevant academic background is a positive signal of the specialist’s depth and commitment to his or her area of expertise. Publications and presentations are also helpful indicators of this. When I evaluate specialists in an area where I have generalist knowledge, I expect them to humble me and teach me something new.

Conclusion

Of course, the ideal data scientist is a strong generalist who also brings unique specialties that complement the rest of the team. But that ideal is a unicorn—or maybe even an alicorn. Even if you are lucky enough to find these rare animals, you’ll struggle to keep them engaged in work that is unlikely to exercise their full range of capabilities.

So, should you hire generalists or specialists? It really does depend—and the largest factor in your decision should be your company’s stage of maturity. But if you're still not sure, then I suggest you favor generalists, especially if your company is still in a stage of rapid growth. Your problems are probably not as specialized as you think, and hiring generalists reduces your risk. Plus, hiring generalists allows you to give them the opportunity to learn specialized skills on the job. Everybody wins.