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Expert Interview with Dave Mayer About Finding Quality Big Data Job Applicants

Photo: Dave Mayer - Founder and CEO of Technical Integrity.

Dave Mayer – Founder and CEO of Technical Integrity

What does it take to be a great data scientist?

Along with the new technology, Big Data comes a new job title: the data scientist. While these professionals are not exclusive to the realm of big data projects, the data scientist is complimentary because of the immense breadth and depth of the data analyzed. The data scientist is another step in the evolution of the data analyst. The training is much the same, with a strong foundation in computer science, applications, modeling, statistics, analytics, and mathematics.

The data scientist is differentiated from the data analyst by their strong leaning toward the business applications of the data, together with their ability to communicate the analytical results to both the business and the IT side of the operation. This helps savvy organizations approach business challenges with more insight and a better chance of success. Great data scientists don’t simply address business issues, but also help the data deliver true value to the organization.

How hard is it to find a good data scientist, and what are the consequences for making a poor choice?

Finding great data scientists isn’t necessarily difficult; getting a hold of them and attracting their interest at the right time and location is the hard part. Companies should consider less experienced data scientists with little production level experience; if you’re open to doing a little hand-holding in the beginning, you are potentially able to help this person grow personally and professionally in a manner that fits your organization,s needs at the same time.

Consequences can be dire in making a poor choice. Fast Company has a great infographic on the costs affiliated with making a bad hire. It’s interesting to note that consequences go far beyond the 25-50k that it may cost an organization, including poor employee morale, time lost in training and recruiting another hire, and loss of productivity.

What are some of the mistakes businesses make when hiring data science professionals?

Many organizations hire non-technical recruiters (internal or external) and they reach out to anyone with “Big Data” or “data scientist” titles on their resume – and that’s a big no-no. The hiring manager absolutely MUST make the time to educate the recruiting arm of the business as to the reason for the need, the projects they will be working on, how and why decisions are being made on that particular team, and so on, so the recruiter can truly digest the role and portray it accurately to any prospective candidate. They need to further empower the recruiter to get the answers they need in short order to keep candidates educated and engaged.

How should a business go about hunting for the elusive data science talent they need?

The search for a truly talented data scientist is multi-faceted. The first step is for the VP/Engineering manager to put themselves in the shoes of a talented engineer/data scientist and understand what is most important to them. Attracting top talent has everything to do with being genuine and forthright and even empathetic in your approach. Culture, team dynamics and technical challenges are at the top of the list for any great data scientist. Of course, pay comes into play and you need to be competitive; but providing really interesting problems for engineers to solve is paramount. You want these folks to be excited about coming to work!

Any great organization has a strategic partnership with experienced recruiters that can help maintain continuity of process with candidates from defining the role to sourcing, and from interview scheduling to offers and on-boarding. A gap in any of those parts of the process can be costly. You never want a candidate to fall through the cracks and end up saying bad things about your organization because you were simply disorganized and unable to follow up appropriately. Treating candidates as you would your most valued customer is paramount.

What are some of the most common mistakes businesses make when hiring data scientists?

Listening more than you speak is important at a base level. It’s super basic stuff, but you’d be surprised how often this doesn’t happen. If you’re serious about building a great team, you need to tell your story and then shut up. There will be plenty of time for technical vetting. Take your time and be open and honest in your approach. Withholding information will only burn you in the end.

How is hiring a data scientist different than hiring for other positions?

Hiring great data scientists is not like hiring for a senior software engineer, though many companies treat it that way. It’s a mistake to treat hiring data scientists as “just another engineering hire.” Their skill sets are often completely different, the things that drive them are often very different, and of course motivations for each candidate is different.

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