Subscribe to DSC Newsletter

The Dangers of the "Talent Shortage" Myth

Every time a new technology disrupts the job market a "skills shortage" is debated between economists and politicians. The story isn't new - 10 years ago recruiters were asking for programmers with 10+ years experience in Java. The gap is widened by non-technical recruiters employing rigid traditional hiring practices. The truth is that there are all kinds of smart people with relevant skills that don't fit into HR's pigeonhole - we're generalists, not specialists. The onus is on the candidates to overcome this myth and sell their abilities as a member of a Big Data team.

How do I enter the race?

Some things never change: cold hiring is still rare. You have to play the popularity game. Your job will probably come from a friend neighbor, classmate, or family connection. Facebook can increase your network, but social media 'friends' will only help you find a job if they actually know what kind of worker you are. Social coding sites like StackOverflow and Github are biased towards stars - the payoff only comes to a few shining profiles.

Some recruiters have confused Github repos for resumes, but your hobby project does show that you're emotionally invested. The wild web is full of gems waiting to be mined for fun and profit. Stick to a subject you know and stay away from things that are overdone. We don't need yet another tag cloud - unless you have a way to make them more useful.

A blog gives you the freedom of using your own voice, which is best used sparingly. Save up your best thoughts over time and release them in a coherent series on the appropriate forum. I keep my LinkedIn and Twitter feeds uncluttered, so the links to these posts are easily found by recruiters. Thank you to everyone who has liked or commented one of my articles.

How do I get past HR?

The most effective way to cut through HR is to have exactly what the hiring manager is looking for. Describe your skills as specific and uniquely searchable as possible. Grepability is very important: semantic overload is search engine kryptonite.

Libraries, not languages.

Old style resumes don't cut it anymore. A list of languages is meaningless. Write the libraries you worked with instead. For example, writing "lxml" provides much better information than "parsing xml in python."

API calls, not libraries.

Libraries that are very popular are equally problematic. It's best to list the API calls you used the most. Instead of writing "Hadoop", try "hadoop.mapred.MapRunnable". Now the hiring manager instantly knows what your core experience is. When they search "MapRunnable", your name will come up right away. API keywords is a major improvement for next-gen talent search engines. When the engine finds your blog posts that have code snippets with "MapRunnable", your name will bubble up to the top.

Geeks understand social media's privacy ramifications and sign up for it all the same. Facebook and LinkedIn pay the bills by serving up our personal information on a platter. When a head-hunter calls, s/he knows who I am, where I live, who my friends are, where I've been, and what I've done. All they need is my full name and my StackExchange/Github/Twitter handle.

Here is my current CV:

{firstName: "Peter", lastName: "Higdon", username: "McPedr0", qualifications: "i m smrt"}

Ok, so I'm not on the hunt, but still - I'm only half joking. Analytics companies proclaim ability to search through vast records to profile and predict winners. They can't find talent??! Such meme-worthy irony is why slashdotters troll "Data Science" as buzzword soup and empty hype.

The trolls aren't wrong: there's a lot of people that think being a data scientist means dumping everything you can find into Hadoop, running a few R scripts over it, and making a couple pictures with Spotfire - overpaid script kiddies soon to be outsourced or replaced by an intern. Miko Matsumura's "Data Science Is Dead" makes a compelling argument against fostering a "rotting whale-carcass of data". In order to be taken seriously as a profession, we need to standardize credible work-flows, foster successful transitions of existing talent, and discourage confusing new marketing inspired words like "thick data".

Who are the "hidden" candidates?

One of the beautiful things about working with data is that it always provides concrete context. If you intimately know your data, you don't need to be a statistician to understand the analysis. That's how baseball fanatics turned into actuaries 60 years ago and it's why hedge funds hire poker players today. Data context allows knowledge workers to transition from anywhere that is data intensive... which is nearly everywhere these days.

The field is less than a decade old and requires cultivation. The only true shortage in this story is the sort of employer investment in skill development that our parents enjoyed. Financial incentives are so strong that new learners and trainers enter the field every day, ensuring that managers will get their chosen flavour of pre-packaged workers eventually.

In the meantime, recruiters need to replace outdated search engines and relax candidate criteria.

There is no "talent shortage", and spreading the myth has consequences:
- Undermines professional credibility with ironic self-contradiction
- Excuses management from investing in employee training to bridge their gap
- Perpetuates unrealistic expectations of the "ideal candidate"
- Bars entry for good candidates with relevant skills that don't fit into HR's pigeon hole

Views: 2038

Tags: hype, resume, skills, talent

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Videos

  • Add Videos
  • View All

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service