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4 Mistakes to Avoid When Hiring Data Scientists

As employers of all industries and sizes build in-house data science teams, these researchers are in demand. Employers spend a significant amount of money and time finding top talent, but they may make errors that keep them from building a successful analytics team. Below, readers can learn more about the mistakes to avoid and they can pick up a few solutions that can provide insights into an improved hiring process.

Listing an Inexact Job Title

The best job posts begin with a good title. While “data scientist” is a bit of a catch-all term, it can be difficult to define what a company is looking for. While non-specific titles can result in a greater response rate, a detailed title can save companies time by helping them weed out candidates that don’t meet their needs.

Not Emphasizing the Right Problems

The most effective data scientists are drawn to challenging issues as well as technology tools and algorithms. When looking for a candidate, companies should emphasize the intellectually challenging aspects of the problems to be solved. Present opportunities not only as a way to move a company forward, but for the candidate to learn in a way that wouldn’t be possible with another company.

Having a Narrow Definition of Experience

Data science as a term was only coined a few years ago, and it has recently seen more mainstream acceptance. When employers look for experienced data scientists, they often want someone who has held the job for years. However, researchers in other fields often do much of the same work, and they represent an under-utilized talent pool for companies. Clients should have in-depth conversation with candidates that go beyond the position’s specific needs.

An Undifferentiated Sourcing Strategy

Qualified data scientists are in short supply and high demand, and a well-planned sourcing strategy is essential in attracting the ideal talent pool. These strategies take time to implement, and companies should invest in them ahead of major hiring needs.

Hiring data scientists is hard. It takes a significant amount of time and strategy to find the right talent, but employers can take certain steps to make evaluations and internal processes more effective. By avoiding these mistakes, companies can set themselves up to build a solid talent pool.