Data Scientists: Before You Go All Crazy About Data, Here’s What to Know About Recruiters

Nowadays, too many platforms and tools are claiming big data or AI-driven recruiting. There is a common pitfall here: a data scientist is not thinking in a recruiter’s way. How could we expect them to solve recruiting problems then? Before data scientists start to truly understand recruiters, don’t expect that big data can bring any miracles to your recruiting. Big-data driven recruiting can be fake unless data scientists truly “get you.”

Understand the Pain Points of Recruiters

As a data scientist, you can consider yourself an expert. No problem! Just acknowledge that you are not smarter than recruiters in recruiting. Listen to recruiters before you start to do your magic. Start with a conversation. You will find that recruiters are really nice people to talk to and that they have a lot of stories.

The conflict between data scientists and recruiters, on paper, is actually pretty easy to understand. We may have received very different degrees and were taught to think in very different ways. Recruiters look at the world as a big pool of people. Talent, skill, experience — these are the ways we categorize everyone we see. Words pour past our eyes as we scan thousands of emails and resumes looking for key phrases and fulfilled requirements. In contrast, if I’m understanding you right, data people look of the world and see not just people but statistical models, averages, norms, and outliers — what is the same, and what is not? Where does this person fit or why not? Part this, part here, part that, part where.

So to find a happy medium and a place to connect, think of the places where you overlap. Recruiters need to process huge amounts of information — that’s what data is all about! Finding quick ways to categorize things and understand people and experiences — data people already sees the world in that way. They find a way to bring it all together accurately, without losing what is really important, and constantly improve the way they do it.

Start that conversation, and begin to process those numbers a little differently. You’ll both benefit from it.

Reframe Data in a Way That Is Useful to a Recruiter

Big data must be valuable data before it starts to be powerful. Consider the problem of hiring a Principal iOS Engineer, from Mountain View, California, who is an expert in social networking, reputation management, and additionally, a bona fide basketball fan (a real challenge that our company faced as our client was one of the biggest NBA stars today).

There are many ways to approach this using data. You could find everyone who was an iOS Engineer to start with, and maybe quantify their profiles with a score that considers all the qualities you were looking for. The problem is, recruiters cannot score each candidate with a grade that says “fits” or not. They have to analyze the whole picture — iOS experience is important, but people’s years in the field are indicative of how good they are. We once had someone who claimed to have Swift, iOS’ programming language, experience for six years. If that had been true at the time, unless he was the architect and creator of Swift, he was a time traveler, because Swift has only been around for two years since 2014.

Then they have to compare their social media and reputation management rating, as well as their references and evidence of their talent, and weigh in what they know of their basketball fandom. And then factor in the current location or willingness to move to consider how “Mountain View, CA” they are.

While this can all be quantified in data, it’s not a black-and-white score or consideration. As a data scientist, these are ways you need to be aggregating and weighting your data to really be of service, to really create valuable data, for a recruiter.

Solve Real Problems

So often a software or a tool overclaimed its capability. A good example is when big-data driven platforms are just a data aggregator of social networks and not much more. While these are certainly helpful, they are a far cry from the ultimate recruiting solution. Everyone claims to have solved recruiter’s problems but so far, they really have not.

Big data should be not only “BIG,” but also “smart.” Seventy-two percent of companies say that candidate quality is very important or critical in evaluating the overall quality of talent acquisition efforts.” To know a talent’s social profiles like their Linkedin, Facebook, or Twitter pages, or even getting their contact information, will not solve your recruiting problems — because recruiting at it’s best is not more contacts, it’s the best contacts.

The way to manage this is to examine the remaining problems that recruiters have. Your data should encompass their major issues without leading them down too many rabbit trails. Profile information, for instance, should be adaptive. While a Facebook or Twitter profile may be perfect for determining social media superpowers, github or bitbucket profiles are a much better assessor of coding and engineering abilities.

What Is Truly Missing in the Recruiting Industry

These days, I am thinking about this question frequently: what is truly missing in the recruiting industry? I am challenged by many of my friends who are experienced recruiters with the same question, too.

A database?

Do we need a better talent database? With LinkedIn, Connectifier, etc. we can search almost every employee in the market. It’s hard to make better database if the current talent database can support sourcing and recruiting well.

A platform?

If the database is large and completed enough, do we need better channels to reach out to talent? Do we need a better platform?

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No. LinkedIn is the dominating social platform for professionals. We can freely connect to and reach out to talent. Beside LinkedIn, we have Facebook, Twitter, etc. for being connected with others. Furthermore, email and phone number can be easily bought from a lot of channels. A platform is not truly urgent need of the job market. After all, a professional platform is not easy to build up at all.

A management system?

There are over 128 ATSs in the market. The problem is which one to choose, as well as the effort it takes to for recruiters to use it.

What’s truly missing then?

Still Pain Points

There are a bunch of software tools and platforms to help recruiters hire fast, and Dean Da Costa has them all. However, there still remains the stupid question: “do recruiters still have pain?”

My personal opinion, evidenced by my time in the business, is that the biggest burden is in finding the right person quickly who is also interested in what you’re offering. Neither by itself is enough, which is what many of these platforms strive to do. The fact that there are millions of job seekers available does not make them fit for your position. The idea that you’ve found the next Steve Jobs with all the qualifications does not mean he’ll want to leave a cushy job at Apple for your low-paying startup who needs someone by yesterday.

Parting Thoughts

Data scientists and recruiters of the world, unite! There are so many ways you can be more understood and useful to each other. Start with the conversation, and build your data and tools accordingly. Understand each other and build something fantastic together.

All in all, let us all turn back to the start point — understanding recruiters and recruiting.

I would like to hear more voices from our recruiting community. Am I right? What do you want that is missing? Is it better tools or better training or complete operations overhaul? What is your interpretation?

If you enjoyed or found this article useful please recommend and share. Let me know your thoughts and leave your comments below please if you have something interesting to share.

Ninh Tran is the Chief Relationship Builder of Hiretual, a “Recruiter’s Best Friend” and an AI recruiting platform of choice, built by recruiters for recruiters, that continually proves to make the Internet recruiting friendly and your life easier. Ninh is also a SourceConERE Media, and Recruiter.com author, and has spoken on various subjects such as “AI & the Future of Recruiting”, “Today’s Sourcing Technology and Skills”, “Recruiting Workflow Automation”, “Hacking Authentic Leadership for Growth”, and “Selling is Human in the Digital World” at University of California, Berkeley, Stanford University, and global TA conferences. As Cal alumni, Ninh founded Trucksome to help emerging local food economies thrive while creating thousands of jobs for the unemployed right here in the US. Then he went on to Google before co-founding an executive search firm HireTeamMate where he leads the business and recruiting operations that placed hundreds under one year before founding Hiretual.
 
Reach Ninh via Twitter: @NinhTran09 

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5 Comments on “Data Scientists: Before You Go All Crazy About Data, Here’s What to Know About Recruiters

  1. Ninh, excellent piece. The greatest pain point that goes ignored is the differnce between finding and recruiting. So many tools and platforms have been developed to find, sort, store, contact potential candidates, but no tool will make a recruiter more effective at truly recruiting those candidates.

    1. Thank you Jim! I totally agree with you on that. Tools can help in terms of making the sense of data and people. They make redundant things easy and faster, but when it comes to the actual recruiting they fall short. Once you pick up the phone or are about to send a message, it’s the old-school outreach and sales skills. The best recruiters know what makes people tick and know how to set themselves apart from everyone else. Take Mike Chuidian for example: https://www.slideshare.net/secret/2iwWG8BlmTqolv

    2. Hi Jim, excellent comment! Could you help to detail “make a recruiter more effective at truly recruiting those candidates”? Thanks.

  2. Everybody has their preferred method of contact and I know most everybody here is keyed in on technology and I get all that; appreciate i.e. – respect it but let’s back up, scroll up and think about, talk about the real problem we’re having here and that is connecting with and starting a conversation with people who we all seem to think we know who and where they are – I say we don’t and that we’re kidding ourselves on all this accumulated “data” that’s clogging the systems that we’re all so googly-eyed over and so Master-of-the-Universe feeling like we-have-it-all-going-on over we just gotta get-a-handle-on-this- one-last-thing…. I say hold on a minute the whole thing morphs as fast as we get it into the damn machine so what are we going to do about THAT and what is THAT doing to this whole issue and how is THAT changing what’s coming out on the other end that’s affecting our messaging to the candidate and, and, and… see where I’m going here?

    Flaw#1 What IF? The current talent databases can NOT support sourcing and recruiting well? What happens then?

    Flaw#2 What IF? The current platforms are not really truly freely connectable?

    The PAIN in recruiting (and sourcing) lies in individuals not being willing to make the investment in themselves of doing the (both physically and emotionally) hard work that is required in any kind of business where connections are required to be made between parties. Unless the very flesh and blood nature in the DNA of of human beings is prepared to undergo some complex sea change that I haven’t heard about I don’t see technology changing the old-fashioned “Hi, my name is…” and the handshake as one of the best icebreakers to initial introductions in the coming years.

    I suppose what I’m saying here is I’d like to see training on SOFT SKILLS in the industry – what I call the Facey-Face stuff.

    1. Maureen, thank you so much for your comments. Outreach is currently the bottleneck of the whole industry. Big data and social aggregators have essentially given our industry the power to reach out to people via different channels, and the chance to research people across social media prior to outreach. Reaching out is pretty simple. You press the button and the message is sent, but on the research frontier there is still much to do. For example the new ‘Open Candidate’ feature tells you who is open to talking about new opportunities and who is not. Social aggregation and ‘Open Candidate’ tell you something valuable about the person, but there is still a LOT that is not covered by big data. The thing which makes big data and social intelligence valuable is that they are a scalable form of recruiting intelligence readily available and relevant to a newbie recruiter as well as a seasoned professional. Big data is a fancy way of saying a lot of useless data. Data science’s role is to make sense of this data and make it useful for the end user who are in our case us, the recruiters.

      Flaw #1: Talent databases alone don’t support sourcing and recruiting. It’s the recruiters and sourcers who actually reach out to the candidates that do the work. Even for all the solutions that claim to fully automate recruiting there’s a person overseeing the whole process – the advertisements vs. the actual product.

      Flaw #2: This is a very interesting topic in itself, perhaps for another discussion.

      From Allison Kruze and Glen Cathey, bad or lack of training and “it works” rivals for number one spot for why recruiters spam. (https://www.slideshare.net/secret/4GUTlxUoTykgnM)

      From Mike Chuidian, who achieved on average 84.41 percent response rate from people he reached out to, I learned that people are lazy, and that’s just a part of the human condition. (https://www.slideshare.net/secret/2iwWG8BlmTqolv)

      Technology has it’s place and with deep learning, things will get a lot more accurate and useful. There are limits that are impossible for even the best of technology to break. I agree with you. Soft skill training and specialization is absolutely needed and will be more and more as technology becomes more prevalent. Have you ever noticed that commission recruiters have better soft skills than those who are on salary without any performance incentives. Simple, they have to sell harder and work harder to earn their money. You are right on point.

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