How to Become a Data Scientist – Part 3
This is the third and final part of a thorough, in-depth overview of becoming a data scientist, written by a recruiter in the field. This part focuses on the job market.
By Alec Smith, Data Science Recruiter.
CHAPTER FOUR: THE JOB MARKET
Time for reflection. If you are ready to gain employment in data science, think how far you have come. You started with an aim, and with a tremendous amount of effort and dedication, you have turned that aim into what you are today: a data scientist in waiting. But don’t uncork the champagne yet, because there is a very large hurdle to conquer: the job market.
As a recruiter, it is easy to forget that most people do not have the exposure to see how this murky world ties together, and so for the inexperienced, it can be fraught with pitfalls. As such, my intention is for this chapter to act as a beacon of light, so you – the employment-seeking data scientist – can approach this challenge equipped with all the know-how you will ever need. But before we dive in, there is a crucially important message to digest:
It isn't enough to have the capability to do the job in question; others will also be qualified and they will probably have more relevant commercial experience. So you can't stop at simply being able to do a job – you have to go further.
This chapter will cover how to ‘go further’ and explore why this is necessary, starting with arguably the biggest roadblock affecting those with experience in other disciplines, or the academics out there with industry aspirations.
A brief note before we get into it: gaining employment isn’t the only route to commercial data science, as you might want to leverage your expertise to launch a start-up. While I applaud the enterprising readers who follow this path, this a whole topic in itself, and one I am not qualified to advise on in this post.
The Experience Bias
Even if you possess the technical capability or the potential to learn very quickly, most employers have a strong bias for hiring those with commercial experience. Although the logic is questionable, it is generally because businesses aim to hire individuals who will be up to speed in the quickest time possible, and those who are least likely to be a failed hire. Employing someone with relevant commercial experience is seen as a simple way to mitigate this risk, not just in terms of capability, but also in regards to ‘culture fit’ (more on this later).
There are exceptions of course, and you tend to find the more enlightened data science teams concentrate on core problem solving ability, rather than experience in itself. This makes a lot of sense, because an interview process that effectively filters for problem solving has the benefit of removing human bias associated with an individual’s background. Thus, it is possible to unearth stars that will probably be undervalued, or even ignored by the rest of the market.
Sadly, there are not many teams that operate like this in Australia – where I ply my trade. Perhaps this is different elsewhere, but here, the industry is still developing and there is not a huge start-up scene, so most data scientists are employed in incumbent industries where their work is heavily affected by bureaucracy, politics and legacy infrastructure. Subsequently, there does not appear to be enough market demand to cause a significant talent shortage, which might drive organisations to consider those with different backgrounds *. I cannot comment on the relative levels of supply and demand in other regions (and whether this has an impact on hiring patterns concerning those without commercial data science experience), but if any informed readers have any insights on the matter, I would be fascinated to find out.
* Disclaimer: I wrote previously in Big Data, Data Science and Analytics in Australia that there was in-fact a talent shortage in Australia. While this may well occur in the future, my observations since the time of writing have contradicted my initial research.
A quick word on junior positions, i.e. hires that require little or no commercial experience: I have had numerous conversations with experienced individuals who have asked me to consider them for junior positions, as a way to break into data science. Fair hiring practices aside; the issue here is teams usually prefer employing younger individuals in junior roles to avoid upsetting the team dynamic, although I doubt anyone will publicly admit to this. As a consequence, the experience bias doesn’t really affect graduates in the same way it does with experienced individuals from academia or other relevant disciplines.
With this in mind then, how can you counteract this bias, if you have the capability, but not the background in industry?
Kaggle / Open-Source / Freelancing
If you read Part Two, you will know we have been here before. And we return for good reason. Competing in Kaggle competitions, freelancing and contributing to open-source projects are not just ways to learn and improve; they also evidence and promote your capability in a way that an online course with Coursera/Udacity/edX never can.
This is especially pertinent if you score highly in Kaggle competitions: people take notice. In short: this is probably the best way to nullify the experience bias, so what are you waiting for??
CV / LinkedIn / GitHub / Blogs
To evidence and promote your capability, add all this to your CV, LinkedIn and GitHub, if you have a profile. When I look at a CV for the first time, I am looking to understand the person’s story: what is their background and how have they developed their expertise? Make it clear. And include a concise profile summary explaining your background, your skill set and your objective. It all helps. Your CV does not need to be two pages, but at the same time, it shouldn’t be an essay, so try and strike a balance. Finally, if your written English is not the best, ask for help – first impressions matter.
On their own, those online courses will not be enough to convince any employer that you are better than other applicants (and everyone completes those courses). So think about your strengths and how you can differentiate yourself. Have you got topics you want to write about? Why not start your own blog? Or you could post on LinkedIn Pulse, or approach KDnuggets or Experfy, as I have done here.
Thinking about all this stuff is important, because – chances are – you will be up against people with industry experience. To revisit the message we touched on at the start of this chapter:
The fact that you have the capability to carry out the job in question is irrelevant if your competition is selected for interview, and you are not.
And this is a very real risk because you will probably be facing competition from data scientists with a track record in commercial organisations, and very plausibly with pertinent domain/industry experience as well.
To reinforce the point, I have spoken with many aspiring data scientists who are dumbfounded as to why they have not been selected for interview, when they are certain they have the right expertise. But what does it matter if you do not even get through the door? And remember – there might be a recruiter acting as a gatekeeper, who might not understand why an ex-physicist (for example) might actually be appropriate.
Provided you can evidence and promote your expertise effectively, the best way to beat your competition is to simply be better than them. Clearly, this won’t always be possible, but if you aim to be the best you can possibly be, you will give yourself the greatest chance. But this is not to say you have to be a better data scientist than your competition to land that first role – there are many influencing factors in play. For example, you might encounter one of the more enlightened teams, there could be a market shortage of data scientists in your region, or you might just hit it off with the individuals you interview with. Or perhaps you are recommended through your own network, which we will look at in the next section.
To wrap up this discussion with some perspective: there is no value in fearing the competition. Embrace it. Understand what you are up against. By accepting this, you can then focus on what is in your control, i.e. not only getting better as a data scientist, but also evidencing and promoting your capability via every channel possible. And if you do this right, you will help level the playing field. Tell your story!