Recruiting for the Data Sciences

So you know what you need in a good data scientist, but do you know how to evaluate these fundamental skills? What resources are available to support your decision-making process?

With the ever-changing landscape of data, analytics and digital transformation efforts, knowing how to identify good candidates is critical. 

Approaches to Hiring:

For experienced hiring managers selecting candidates can feel routine. But challenges persist in ensuring that incoming talent is the right fit for your team or organization. Do they truly have the right skill set? How likely are they to grow into the role -- or even future leadership positions? 

These are especially large complexities for technical or analytics roles. 

Here are some ways to ensure you get the right fit on your team.

Data science as a practice has exploded in the last 15 years, and is expected to continue its rapid growth in the coming years. This growth is supported by an ever-growing number of academic programs, training curriculum or mid-career options available to enter the field.

And, despite the economic uncertainty of the moment, companies remain focused on data as a foundational aspect of their business strategies, reinforcing the level of demand for Data Science as a necessary part of their operations. 

This explosion has been mirrored by an evolving landscape of tools and technologies, including new languages, statistical software, and data wrangling tools.. Data Scientists themselves drive much of this evolution, as they look for new and more efficient ways to perform their work. 

Industry experts and solution architects also contribute to this new landscape as they develop more powerful solutions, and many of today's data-mining, statistical or visualization tools represent dramatic improvements from the early years of data science.

But, while this increased availability of tools has made the lives of data scientists easier in many ways, it can make it harder for organizations to know how to properly evaluate incoming data-focused talent, or up-skill existing employees in the right way.

Here are some tips to consider as you look to find or grow your organization's data science talent and capability set. 

The advanced analytics space is all about results. Your company expects the data team you built to deliver actionable insights that improve the company. However, when that same team fails to achieve the desired results, you get the brunt of the blame.

The market for data science talent is tight, and it doesn’t give you much range to find top talent. Sometimes it seems like finding qualified analytics professionals is like looking for a needle in a haystack. Even when you think you have hired the right person, you are left scratching your head when that person turns out to be a bad hire. You ask yourself, "what am I missing? What do I need to change?"

Here are some things to think about during your next data talent recruitment.

An Expansion of Analytics-focused Role

In response to the huge growth in data and its increasing importance to organizational strategy and growth, a variety of roles have been created to manage and support organizational efforts. This has created a growing set of opportunities for analytics professionals to build a career focused on data, strategy and analytics.

These roles run the spectrum of needs, from data engineers responsible for database design and data acquisition, to data scientists focused on exploring captured information and deriving insights, and business analysts and analytics managers leveraging data to monitor organizational performance.

These roles – in particular data scientists – enjoy some of the most competitive compensation and widest array of opportunities for new professionals.

As a recruiter, you dread this situation: After the first month on the job, the new hire turns out not to be the strong candidate they exhibited in the talent acquisition process. You hope that, perhaps, they only need additional time to adjust. Still, when the hiring manager tells you that the new hire doesn't seem competent for the role, your stomach drops.

Indeed, something went wrong. Like with other positions you recruit for, you work with the information the hiring manager gives you - what are the required skills and experience, what is the budget, where the person will work, and to whom they will report. You depend on what the hiring manager tells you and prepare a job posting from this input. When it comes to data analytics roles, however, without having a background in the field itself, it is a challenge for most recruiters and HR departments to discover why the people you hire turn out not to be strong in the competencies required for the job.

In the data specialist space, two types of bad hires sneak through. First, some hires end up not having the skills they claimed to have during the hiring process.

And second, there are the new hires who were hired based on a poor job description, which originated with the information provided by the hiring manager.

Addressing these two concerns can help you improve recruiting for and, more importantly, hiring for competence.

The hiring manager tells you, “Another new hire isn’t meeting expectations.” New hires who don’t pan out not only foil the trust in the recruitment process but costs the business money. If you work for a small business, the cost of a poor hire could reach $8,000, and up to $240,000 for larger firms. These numbers do not include the costs of reduced productivity, damaged brand, decreased employee engagement, and increased management time spent on fixing under performance.

As a Recruiter, you work with the information you are given - what are the required skills and experience needed, what is the budget, where the person would be working and to whom they would report. You depend upon what the hiring manger tells you and have to prepare a job posting from this input. Without having a technical background, it is a challenge to discover why the previous hires weren't considered good hires.

But do you realize that there's an additional reason why your job is so hard?

Hiring managers' job descriptions are so technical, it's hard to feel confident in finding good candidates. Help.

Job descriptions for analytics specialists are ever evolving and can seem inaccessible. Consequently, the specs for the needed work become a bottle neck in your way of recruiting the right data or analytics candidate. The way the hiring manager wrote the job posting puts you in a tight spot. You know it should be modified, but without being a data scientist yourself, what can you change without attracting the wrong candidate or delaying the process. Also, you lose confidence in your ability to find the right person. Here are some tips to help you translate your hiring manager's requirements.

Yes, your company is having a hard time hiring qualified data scientists, and there are two reasons why.