Hiring Data Scientists - Best Practices & Job Description Template

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Occasional Contributor

Hiring in data science is one of the single most challenging tasks within human resources. Today,

demand for data science professionals is outstripping supply at an astonishing rate—making the challenges of onboarding and retaining high-quality professionals acutely difficult. The most common approach to finding and hiring data scientists is to hire them away from positions at rival firms.

Doing this well, however, is something that even the biggest and best tech firms in the world struggle to get right.

Even the careers of some of the biggest names in data science include a long and varied list of companies and positions as part of their resume. Ian Goodfellow, a leading expert in deep learning and creator of the neural network library Tensorflow, has been hired twice by Google, OpenAI, and Apple in just a handful of years.

Creator of the Python programming language, Guido van Rossum, has had a similar trajectory moving between Google, Dropbox, and Microsoft during his career.

What these cases show is that even the tech giants are struggling to find the right formula for hiring data scientists and retaining high quality teams against lucrative external offers. Within data science, the task of finding experts is necessarily more challenging due to the extensive academic and professional requirements necessary for the role.

Is your firm one of many struggling to hire and retain data scientists in today’s market? Tell us what you need! We can connect you with up to 5 companies to match your needs within 72 hours—for free!

 

Finding Experts in Data Science

The field of data science is a broad one encompassing many disciplines and many technologies. This complexity makes finding one single way to hire professionals within the field all but impossible.

Successful hiring in data science needs good forward planning, advanced preparation, and exceptional presentation to attract the right candidates and ensure they can be successful within your firm.

One of the first things you should consider is whether to hire a freelance data science professional or outsource an entire team. With both options having advantages and drawbacks you should consider and each well-suited to different types of projects, teams, and industries—the choice you make is an excellent starting point to engineering success.

Data Science Roles

Even within the role of data scientist, there is further diversification when it comes to the type of engineer you hire. There are a host of unique specializations, expertise, and domain-specific knowledge sets in areas most won’t have even heard of outside the field. Then, there are the generalists who can turn their hand to most things and help out where skills and knowledge are needed.

Knowing a little more about both roles can help you to make better decisions when it comes to hiring data scientists.

The Generalist

A generalist typically has a broad base of knowledge on most fields within their area of data science. With an understanding of the available tools and how to apply them, generalists can get a great deal done to advance a project towards its goals.

An essential part of any successful team, generalists are typically exceptional at solving problems and making tangible progress towards understanding and solving the project’s biggest challenges. An example of a notable outstanding generalist is machine learning and AI lead Andrew Ng who has worked on deep learning, natural language processing, and computer vision amongst other fields.

A good generalist typically makes for an outstanding leader in data science teams and finds solutions to the day to day problems that occur over the duration of a project.

The Specialist

Specialists in data science are typically focused in on solving problems in an individual domain. The specialist data science engineer is most often equipped with the latest tools and techniques within that domain and an expertise in solving problems to get a project up and running with the relevant toolsets.

Teams building a sophisticated and novel chatbot application, for example, are likely to need at least one specialist in the field of natural language processing. An example of this kind of specialist would be NLP researcher Tomáš Mikolov who has been working on language models and text frameworks for over the last decade.

Creating the team from the right combination of generalists and specialists is vital to creating an environment that prizes high-quality data scientists and retains the best talent long into the future.

 

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