3 Data Science Hiring Best Practices

The demand for Data Scientists is growing at a rapid pace in today’s highly competitive tech landscape.

In fact, one recent survey by First Round Review found that in a competitive field like Data Science, good candidates often receive three or more offers, which is why the success rate of recruiting top talent is typically below 50%.

What’s more?

The 2020 Yello Interview Scheduling Survey found that on average, recruiters are spending 2/3 of their overall recruiting time on the interview process itself.

Even if you’re an HR leader with substantial hiring experience in Data Science, chances are finding the best candidates more often than not demands a lot of time and effort on your part.

By designing a hiring process that’s smarter – both in identifying exceptional talent and simultaneously reducing the risk of losing them – it is possible to reserve the best candidates for yourself.

Below, we have outlined a few Data Science hiring best practices that can help hiring managers reduce the overall time-to-hire and increase the company’s chances of attracting top candidates.

Data Science Hiring Best Practices

 

1) Have a Clear Understanding of Your Ideal Candidate

First and foremost, you need to make sure that your recruitment strategy is in complete alignment with your hiring needs. You also need to ensure that you’re optimizing it at regular intervals to make sure you aren’t wasting capital on outdated processes that aren’t bearing desired results.

One excellent way of doing this is by having a very clear understanding of how you want candidates to perform data science and if they are going to fit in well with the existing team in your organization.

At the highest level, you should be clear about the end product your data science team will produce:

  • Will it be prototypes and designs that are given to developers?
  • Or analyses and visuals that inform decision makers about the most favorable choice?
  • Or will they produce applications that can be scaled and used to provide additional support in production environments?

Once you’ve figured your expectations of the end product, the second step would be to come up with candidate personas – a brief prototype of what your ideal candidate looks like.

In one of its recent articles titled ‘The Beginner’s Guide to Candidate Personas‘, Glassdoor goes on to explain why identifying the right candidate personas is important:

Having a documented candidate persona strategy leads to more informed recruiting. How? It’s simple — a well-crafted candidate persona puts you in the shoes of your ideal candidate. You’ll know exactly what the perfect candidate is looking for, where to find them and how to effectively engage with them.

One easy way of doing this is – speaking to data scientists and identify at least five challenges you would want to see your potential hire tackle.

You can then include those challenges in the form of questions within the interview process. For each, make sure that you have the data required, and experts on your team that can visualize a solution that would be effective from different angles.

Having a brief understanding into how the existing team at your organization performs data science, and what challenges they most want new hires to be able to handle can help you design a recruiting process that closely reflects the working conditions at your company.

This means you should put candidates into an environment that closely resembles what their ‘day-to-day’ as a data scientist at your company would be. If they can succeed in that environment during the interview process, then their chances of succeeding long-term are much greater.

2) Design Your Interview Process to Sell to Your Ideal Candidate

Today, the majority of interview processes fail to sell to the best candidates in the market.

In fact, according to one recent statistical compilation published by Lever.co, as many as 60% of job seekers said that they have quit an interview application in the middle due to its complexity or length.

You need to remember at all times that the top data science candidates have ample choices, and they won’t jump through hoops just to apply for an open position at your company.

You also need to place enough thought into optimizing your interview questions!

Think about it: most frequently asked interview questions revolve around human behavior, such as disagreements and conflicts employees either often face, or are an active part of in the workplace. And while the end goal of these questions is to predict future performance of a potential hire, they often instead end up steering negative emotions.

Take, for example, a few popular interview questions:

  • Tell us about a time you disagreed with one of your superiors’ decisions? What did you do?
  • Someone else takes credit for the task you spent hours completing. How do you react to it?
  • Tell us about a time you got into an argument at work? How did you deal with it?

The problem here is that each one of these interview questions calls for one acceptable answer where the candidate either acted in a responsible and calm way, dealt with a problem, or resolved a conflict. In other words, they hint to the potential hire that the person you want on your team is someone who refuses to fail. However, this may also give out the impression that you’re choosing to turn away from candidates who aren’t afraid of telling you they have failed, leaving out a lot of good applicants.

The solution?

Try eliminating problematic phrases and words to solicit bad responses.

For example, here’s how you can rephrase one of the interview questions stated above to attach a positive sentiment to it:

Instead of saying – Tell us about a time you disagreed with one of your superiors’ decisions? What did you do?

Say – What do you do in situations where you don’t agree with the outcome of your superiors’ decision-making?

Modified, open-ended questions like this strip away negative connotations and invite the candidate to be open about their last memorable conflict without the guise of a desired response or resolution.

Yes, the practical reality is that great candidates will never discuss a problem without automatically describing how they solved it. However, open-ended questions like the above give interviewees the space to tell their own story.

This was just one example of how you can optimize the interview process to make it more easy-going and appealing for your ideal candidates.

Try stepping into the shoes of your candidate personas, and make the interview process as hassle-free for them as possible, while also making it easier for you to identify what their strengths and weaknesses could be.

3) Involve Your Team Early On in the Recruiting Process

Lastly, you want to make sure that you have metrics and objectives in place that every single person on your team understands.

Hiring for technical roles can never truly be done in a vacuum.

Involving your team in the process since Day #1 means you increase your chances of establishing clear frameworks for assessing candidates at every stage of your recruitment funnel, and as a result, become more confident in your hiring choices and decisions.

This is especially advantageous because no matter how you recruit, there comes a time when every hiring manager has to make difficult decisions.

Some of the things you can do to instill a stronger sense of responsibility within members of your recruitment team include:

  • Gather direct, one-on-one feedback from everyone on your team involved in the process of hiring.
  • Have an open forum. It ensure that your entire team is on the same line and is looking for the same qualities in the candidates. An open forum also helps to change your recruiting strategy and needs quickly over time.
  • Brainstorm every idea together to assess its practicality and chances of implementation.

Another best practice would be to engage your cross-functional partners, if any, in evaluating the candidates shortlisted by you.

In the process of doing so, you will collaborate with various engineers, product managers, and decision makers. Involving these key partners further ensures that you select candidates that can be successful across departments and divides.

As HR leaders, we all know that hiring, be it technical or non-technical, is an ongoing process.

And while you only get better at it with time, you will surely learn something new with every strategy that you formulate if you keep an open mind and strive to make changes that cater to your organization’s end goal.

To learn more about data science hiring best practices and where you can find the best tech candidates in a comparatively shorter period of time, get in touch with us at Benchpoint.com right now!

We will be happy to help you with the smallest doubts or queries you have in mind, and help you build more well-defined hiring strategies that bear desired results within no time.

You’re sure to receive a higher return on investment on your initial hiring capital when you work with our network of highly skilled and experienced recruiters, spread across the United States.

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