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. 

Not long after you learn that the company is not meeting current expectations, a hiring manager tells you that a data visualizer you'd hired quit. When you ask why, the response is not unexpected, "because there were not enough career advancement opportunities."

You understand that retaining top talent is vital to meeting your organization’s business goals. However, you also know that the data science labor pool is small. Improving retention has to be addressed.

Data professionals, young and old, whether data visualizers, data wranglers, business analysts or other related roles, are likely to pursue new opportunities for these top two reasons: compensation and lack of career growth.

Hiring managers keep telling you that it is taking too long on average to fill data science positions.
In response, you want to create a pool of pre-qualified candidates so that you have a list of data professionals you can hire quickly for short-term or long term projects.

Your hiring manager tells you that another data scientist quit, and it leaves you scratching your head as to why you cannot keep qualified data scientists at your company.

With the tight data science talent market, the demand for analysts outstrips the supply. This increases the pressure to retain your existing data team. While you may not be able to control everything once someone starts in the job, here are some things you can do before they start.

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.

Another day comes to an end with the hiring manager telling you that a new hire isn't meeting expectations. New hires who don't work out not only break the trust in the recruitment process but cost the business money. For small businesses, the cost of a wrong hire could reach $8,000 and up to $240,000 for larger firms. These numbers exclude the costs of reduced productivity, brand damage, reduced employee engagement, and increased time managers spend mitigating poor performance and finding replacements.

As a recruiter, you depend on the information the hiring manager gives you to prepare a job posting and find the right candidates. Plus, you discover that your range of qualified candidates is small. Without having a technical background, and using a small sample size for reference, it can be a challenge for you to discover why the previous hires turned out to be bad hires.

You might think that an Applicant Tracking System (ATS) could help broaden your scope of data talent acquisition. However, you will be surprised to find out that ATS will not help you. Here’s why.

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.

Has the range of your data scientist talent search shrunk to the point that it takes too long for you to find quality analytics professionals? Nowadays, this is common because the demand for these professionals is outstripping the supply.

Quality data professionals possess an array of technical and analytical skills that are in high demand. However, the overall pool of these professionals contains a small number of qualified candidates. You might bring in a large number of candidates to interview with your hiring manager. However, they tell you that the candidate did not fit the scope of skills for which the company is looking.

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.