An ode to the type A data scientist

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You’ve probably heard of the elusive data scientist unicorn by now. They have all the answers to your questions on data analysis, machine learning, product metrics, big data, experimentation, deep learning, business acumen, domain knowledge, and more.

A better visualization of this can be seen in the study below, which polled 400+ data specialists on their comfortability with different data science areas.

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As you might imagine, finding someone that excels at all of these things is next to impossible. Because of this, companies have come up with specialized roles within broader data science fields.

These specializations include titles like machine learning engineer, data engineer, data analyst, product scientist, among other roles.

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As you can see above, there will always be some sort of overlap between these roles. This will typically be specific to each company, their data ecosystem, and their future goals.

But believe it or not, things don’t stop here. We can break down data scientists even further.

Type A vs type B

Many well-respected individuals in the data science community have taken a stab at classifying different types of data scientists. However, none were as effective as Michael Hochster, Ph.D., the former head of research at Pandora, with his answer on Quora:

• Type A data scientist: The A is for analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way. The Type A data scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum: data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.
• Type B data scientist: The B is for building. Type B data scientists share some statistical background with type A, but they are also very strong coders and may be trained software engineers. The type B data scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

These descriptions primarily apply to working with data science in industry, but I’ve found them to be spot-on in my experience.

The next generation

They answer difficult questions

They make the complex simple

They consistently drive impact