Should You Hire a Data Generalist or a Data Specialist?

As you search for a data scientist, determine which will provide the most value to your company right now.
Headshot of Bushra Anjum.
Bushra Anjum
Expert Contributor
August 17, 2020
Updated: August 18, 2020
Headshot of Bushra Anjum.
Bushra Anjum
Expert Contributor
August 17, 2020
Updated: August 18, 2020

Throughout my 10-year career, I have seen people often spend their time and energy in passionate debates about what data science can deliver, and what data scientists do or do not do. I submit that these are the wrong questions to focus on when you are looking to hire for your data department.

In actuality, your current value proposition determines what data science means for your company, and hence the role and responsibilities of a data scientist in your ecosystem.

Instead of embarking on an impossible task to define data scientists in absolute terms, and hoping for an industry-wide consensus on it, think about the role in an alternative way. Define your company’s data needs in terms of data generalists and data specialists.

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Data Generalists Versus Data Specialists

Some entities (be it people or companies) consider data scientists strictly as data generalists, and others as data specialists. But a data scientist can be either. Data science is about using data to provide value (such as money, growth, reputation) to an organization, and to provide value, sometimes you need a data generalist, and sometimes a data specialist.

Data generalists are breadth focused and are highly capable in conducting ad hoc analyses, extracting insights from data, and helping direct business questions. They can function reactively, like looking back at the data and reporting trends, and can also operate proactively, by exploring more open-ended questions, and looking into the future. Their skill set spans exploratory data analysis techniques, scripting and modeling, visualization and reporting.

Data specialists are depth focused and have expertise in automation, optimization, machine learning, and performance tuning. They come in when a problem is well-scoped and a process well understood, and take it to the next level of optimization, enabling operation that requires minimal human interaction.

It is important to recognize that there is no implicit hierarchy between data generalists and specialists. They each focus on a different set of problems, and therefore provide a different set of solutions, while being equally valuable to a company.

At Doximity, for instance, we employ data generalists as full-stack data analysts. These data analysts are highly trained in SQL, and are experts in telling a story about the data. They do both past and future-focused analyses and offer recommendations. They also know enough scripting, programming, and modeling to weigh out different approaches. If and when they narrow down on a specialized problem that requires expertise in advanced modeling and automation, our data specialists, called machine learning engineers, come in. These engineers are a shared resource for the entire data department and help set up and productionize, optimize, and automate solutions. Though different titles, we consider both these roles as data scientists.

Every company needs to determine the appropriate mix of data specialists and data generalists for their goals.

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Define the Role for Your Company

Start with a simple question: Based on your current needs, do you need a data generalist or a data specialist? And then make that expectation known — starting with the job posting.

Instead of copy-pasting requirements from another data scientist job advertisement, or creating one with a superset of requirements from multiple similar postings, it is paramount that the company intentionally defines its requirements. This is the single most important step that hiring companies can do to enable fulfilling careers and enhanced productivity.

For example, if you are focused on providing a single well-defined service, you may benefit from having a data specialist joining your ranks. They will help optimize and automate the task. On the other hand, if your product offering spans multiple domains, having data generalists may be more beneficial. They are better equipped to provide overarching product analyses, monitoring, and making growth recommendations to the business. Yearly targets, quarterly goals, and 3-6-9 planning meetings help us keep track of such needs at Doximity, and adjust accordingly.

So, do you need to hire a data scientist? Before you do, determine which will provide the most value to your company at the moment: a data generalist or a specialist. No matter what you choose to call the role, spend some time defining the breadth or depth of the expectations clearly. It will empower you to make the right hire, and also enable the potential employee to make informed decisions in line with their own goals.

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