Getting your first job in data science

If you’ve never worked as a data scientist before, it’s relatively hard to get that first data science job.  Probably way harder than it should be; more on that below.

Put another way, it’s way easier to get data science job offers once you’re already working as a data scientist.  The classic chicken-and-egg problem.

Some personal background: prior to 2014, I had done some independent data science work – but nothing too formalized.  I also had no advanced degree (just a bachelor’s), so it was going to be relatively harder for me to get that first ‘official’ data science job.

At the end of 2014, I was hired as a senior data science consultant within the fintech industry.  I’m now an employee and team lead.

Some thoughts on getting that first data science job:


Contractor vs. employee

It’s way easier to get initially hired as a contractor vs. hired as an employee – especially at larger companies.  Of course, the flip side is you can get fired way more easily as a contractor, and you essentially have no protections that employees will generally have.

I think there’s a lot of misinformation out there about working as a contractor – which is a broader topic for probably a later article.

However, to summarize: I think there’s some potential advantages to initially working as a data science contractor (vs employee).  And, especially if you’re just looking to get hired for that first data science job – there’s way less friction involved in getting hired as a contractor.

For example, depending on the company, getting hired as employee could involve: a formal resume submission, prescreen conversation, screening conversation, on-site interview, HR ‘fit’ conversation, aptitude test, coding test, full background check.

Conversely, for getting hired as a contractor, it could be as little as two phone calls – and that’s it, you’re hired.


Having business domain experience is a major plus

For me, I think this was the main factor in getting initially hired into my first ‘official’ data scientist role.

I had some relatively deep domain knowledge from my previous work in industry as a quant/market maker for stocks, bonds, options, ETFs, and futures.  At the time, this market knowledge was relatively useful for the role I was being recruited for.


…or at least having a strong interest in learning about the business domain

However, even if I didn’t have that domain experience, I still don’t think it would have been impossible to get hired for that role eventually.  I would have probably had to take at least three or so dedicated months to prepare though.

Now that I’m somewhat in a position to hire people, what I look for is less about “how experienced are you in this business domain,” and more about “how interested and motivated are you to learn more about this business domain.”

We’re all learning (myself certainly included), and I’ve been finding it a bit difficulty in finding people who are genuinely interested in actively improving.


Technical vs non-technical skills

Somewhat generally, I think it can be easier to acquire new technical data science skills vs. acquiring some of the  most critical non-technical data science skills.

Of course there are exceptions, and my sample set is biased because I only generally get to talk to people who had some decent baseline of technical data science acumen.

However, I was discussing this concept with someone from my little data science industry group, and we generally agree.  In other words, we think it’s harder to teach someone to be truly passionate about learning and improving, vs maybe teaching them the latest Scala optimization within Databricks.

For how fast the the field of data science is changing, I think being actively adaptable is much more important than being an expert in any one technical skill – especially with how fast the industry is changing.


Master’s degree programs, online courses, etc.

In my opinion, I think there’s a bit of a bubble in data science education.  Everyone and their dog seems to be running their own data science bootcamp, online course, certificate program, whatever – and I can’t stop seeing ads for data science master’s degrees from all sorts of universities (of varying repute).

Not that these programs are bad – it’s just, there’s way too many of them (the subpar ones), and hence way too many fresh data science graduates with skillsets that kind of just blend together.

At least for me, I’m more interested in someone who has produced something (even if was just a personal project) vs someone who has all the polished ‘credentials’ – but can’t explain why they’re interested in doing data science in the first place.

It’s easy to get a credential – especially if you’re paying someone for it.  It’s hard to actually produce something, and have something to show for it.



Of course, with these thoughts I don’t speak for anyone else except for myself…but I’m guessing more data scientists than not would agree with me here.  Mostly.

It’s hard getting that first data science job – but if you’re willing to think more independently about the whole hiring process…it might go way faster (and easier) than you might think.


The views expressed on this site are my own and do not represent the views of any current or former employer or client. 

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