What Lies Behind Most Data Science & Analytic Resumes
Chaos breeds creativity and gaming… but not the fun kind
IN A WORLD (use that cool narrator voice from the movies) where robotic parsing algorithms evaluate candidates for job consideration — you are going to get some monkey business. Wait… does that really follow?
IN A WORLD where millennial gamers routinely pwn AI exposing it for the rather overplayed, underwhelming “science” it truly is — young people don’t respect AI. So yes, they have no issue “gaming” it. They have literally been trained for thousands of hours to do just that.
IN A WORLD where every analytic skill has twenty names, three definitions, and a whole lot of “wiggle room” — this is just too easy! I can game the AI. I can game the HR. I can game the client layer. But… can I game the other analysts?
Sadly… yes. Or at least more often than you might expect. It is likely about 50/50 for our gamers. If they picked their marks right, smaller companies with small, highly specialized, or non-existent analytic teams — they can skate right in. This will all end badly some other day.
Even when the candidate is eventually uncovered in the interview process, the hiring company is left feeling betrayed, frustrated, and well… sad. In the field of analytics right now, this is a BIG problem.
Do I have a “Big Data” answer? Of course not, that is part of the problem!
Do I have an analytic answer? Of course I do — for candidates and hiring firms alike. But this article is not about the answer, it is about the problem. Feel free to reach out if you need help solving it.
The Problems
They fall into two main categories:
- Analysts recognize that most analytics is learned on the job. So the incentive is to get a job at any cost. Even if it ends poorly, you can just hit restart and move on to the next level…
- Most companies outside the Masters of the Universe (Google, Amazon, Facebook, Apple…) don’t have the resources to properly screen candidates. Let’s break this one down further.
The Problem With Your Analytic Team
There are many. Most are not their fault. All are very real.
- It is small. More eyes are better than less for vetting anything, but especially candidates.
- Their knowledge is specialized. A strong analytic team consists of members with numerous specialties. Using the Data Scientist to interview the Business Intelligence analyst (the stat head to interview the report designer) is of very, very limited value.
- Too many people are falling back on cultural fit!
NEWS FLASH — Your company has a human culture and a data culture! Analysts need to fit both and you just learned the second one exists! Get help!
4. HR is not part of your analytic team. They may want to be, but THE WORLD is not there yet. A small handful of HR (headhunters, recruiters, etc) leaders can, but they are rare. Worse still, they are also quickly promoted — leaving you in the lurch.
5. The interview itself is a really bad time to assess skills. It is much better at cultural fit than data cultural fit. You need to be vetting people ahead of the game.
Consequently
Resumes are full of lies. Pipelines are full of candidates excited to figure it out when they get there. Hiring teams are overwhelmed, under-educated, and fighting on the wrong fronts. The feedback loops are broken. It is … mass hysteria may be taking this a little far.
This article was about sharing a perspective and giving public voice to a now broken record (boy that is an ancient reference) of frustration and complaint. We live IN A WORLD, that is only going to get more confusing, complicated, and creative. Sometimes it is good to detail the why. Thanks for reading!
