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.


5. Other Things To Be Aware Of

Giving Your Approval: Whatever happens, if you provide your authority to be represented, stick to it. Do not then give your approval to another recruiter or apply directly – it makes everyone look bad.

Written Confirmation: When you agree to be represented to an open position, request for the recruiter to confirm in writing once they have processed the application on your behalf (as it isn’t unheard of for recruiters to tell job seekers they have processed their application, when in truth, they haven’t).

Honesty: You do not have to tell recruiters where else you have applied (as they might even use this information to approach that business in the attempt to place another candidate of theirs), but be honest in your feedback and keep them informed of your progress elsewhere – it is only fair.

Salary: Considering the higher costs associated with using a recruiter, you might assume that this will negatively affect your remuneration package. However, in reality, it just isn’t that simple. As an example: if you are in demand from multiple businesses, it usually leads to competitive salary offers, irrespective of on-top recruitment fees. Furthermore, it isn’t really a factor in large organisations as it is generally accepted that recruitment comes at a cost (even if third party agencies are avoided, internal recruitment is not exactly cost-free). That said, of course – in particular circumstances involving budget-constrained businesses, higher recruitment costs might result in lower offers. But honestly – there are so many variables; it isn’t worth worrying about. And remember: on the flip-side, a recruiter can actually have a positive impact on your salary by using their knowledge of market rates to negotiate the highest offer possible (after all, recruiters deal with offers all the time). Finally – you might have never known about the opportunity if it weren’t for the recruiter, so in this circumstance, what is better: a (potentially) slightly lower offer, or no offer at all?

Bias: While an informed recruiter can be a great source of knowledge and advice, always be aware that they have a vested interest in you joining an organisation through their representation. As with any perspective lacking objectivity, it doesn’t mean they will outright lie, but just like you should be sceptical of a nutritionist selling a diet plan, be critical and question their claims. My advice: seek guidance from those you trust who have nothing to gain from your decision.

Apply Direct

With the information you now have in your arsenal, you should be able to leverage recruiters to your advantage (exciting right?). However, not all companies use third-party agencies (especially start-ups) so it is advisable to be pro-active with your job hunt. Research businesses that employ data scientists (LinkedIn is great for this), approach them directly, and monitor where businesses advertise including their career pages. You know what to do next.

Advertisements / Position Descriptions

Both agency recruiters and businesses directly advertise open positions. An important message on the content: DO NOT WORRY ABOUT THE DETAILS! They are often generic, clichéd and list every skill and technology under the sun. This is usually because a recruiter with poor understanding has written it, or it has been rehashed from old versions to save time and is therefore not all that relevant.

My advice then: do not worry about ticking every box; try and get an overall sense of the position to determine whether your skillset is suitable. But beware: it is not uncommon for agency recruiters to post fake advertisements as a way of ‘fishing for candidates’. There isn’t really a good way to spot these, however if the same recruiter continues to have mysterious openings that are suddenly filled or cancelled, then this could be an indication. Or they are just really unlucky.

Internal Opportunities

This should be obvious: if you are working for a business in a different role, say as an engineer, your best chance may be an internal move. This is easier if you are working closely with the data science team, but either way, network internally, make sure you communicate your ambitions, and promote the steps you are taking to develop your expertise.


You are nearly there. And if you have developed enough capability, interviews are nothing to worry about – your ability will shine through. A couple of tips for you though:

  • Communication: As we discussed in Part Two, this is an intrinsic part of data science, and it is one of the most common reasons why data science interviewees are rejected. To repeat then: you will be doing yourself a disservice if you underestimate this skill, however technically advanced you are.
  • Preparation: Do your research on the role, the company, the people, and if applicable, have a play around with the product. Take the time to understand the business, and go to the interview prepared with ideas on how you would employ data science to create business value.

The only thing I will add is this: any real data science interview will test your problem solving, most likely through practical exercises. You cannot game these. If you can, I have no doubt that your role will be data science in name only.


Overlook this at your peril. It is particularly relevant to the wider science field, as Will Hanninger explained:

Coming to the bank with lots of pure machine learning guys, I needed to first learn the language; it’s full of different jargon. For example, a variable in physics means a feature in machine learning, and a set of variables means a feature vector.

If you do not have patient interviewers (Will did), you might find yourself without a job due to miscommunication. And this is the precise experience Sean Farrell had with his very first interview – not exactly ideal.

‘Culture Fit’

This is a term that is thrown around a lot, and sometimes used as feedback to explain why a candidate was rejected following an interview. Obtaining honest interview feedback is so useful, and being rejected due to ‘culture fit’ can be frustrating because it doesn’t seem to explain the reasons in sufficient detail.

While it might occasionally be used as a cop-out when an interviewer simply prefers someone else, usually it is legitimate feedback, albeit lacking on the specificities. If you remember Boris Savkovic’s quote towards the end of Part One, it is clear that commercial data science has very different challenges than many other related disciplines. So ‘culture fit’ is normally code for: “we have concerns that this individual might not work well in our environment”. This could be due to a number of reasons: maybe it is your communication, maybe they think you would get frustrated with internal politics, or maybe they feel your interests are better aligned to theory/research than the work they have.

Either way, if you receive this feedback, go back and push for more specific information, so you can improve for future interviews. And try not to get down if you fail a few – unless you are an absolute superstar, it is rare to get job offers every time.



I set out with the aim of writing something that would cut through the hype and misinformation to help any aspiring data scientist; not only those on the learning curve, but also those embarking on a job hunt. And while it wasn’t my intention, this took me on a journey that questioned the very nature of what we currently mean by ‘data science’.

My hope is that I have done this topic justice, and by doing so, I have achieved what I set out to do. But more than that: I hope it causes you – the reader – to question your motives to ensure you are going down this path for the right reasons. And if you are, I hope you are now equipped with the knowledge to set real goals and begin working towards them.

But above all else, I hope that the key messages are abundantly clear; that to master real data science, it takes time, dedication, persistence, and above all else: passion. Without this, you simply won’t develop into a problem-solving data scientist – however naturally gifted you are. Yes, the market considerations are important, but everything else should be a secondary consideration to the quest of mastering your skills.

I will end on this message: every single person I spoke with worked incredibly hard and ended up getting a break to reach their goal of becoming a data scientist. There are times when it will become tough, but if you have enough passion and persistence, you will get there. And so I leave you with this: GOOD LUCK!

A massive thank you to Will, Dylan, James, Yanir, Boris and the two Sean’s: quite simply, these posts would not have been possible without your input.

Bio: Alec Smith is a specialist recruiter within the field of data science and engineering. The position of an agency recruiter offers a unique, cross-sector perspective of commercial analytics and he leverages this viewpoint to write about various topics within data science, technology and hiring. Originally from the UK, he is currently plying his trade in Sydney, Australia. Follow Alec on Twitter @dataramblings.

This post was originally published on Experfy's Blog.