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The Hiring Process for Data Professionals

Advice from a Recruiter

01 Sep 2022 by Pavel Chernetsov

Photo by Christina Morillo from Pexels

The hiring process can be a daunting task for data professionals, whether you’ve just left university and are seeking a Junior position or even if you are a Senior that is looking for a change. After talking with Alicja Notowska, a recruiter with nearly a decade of hiring experience under her belt, we’ve compiled some key points and advice for data professionals that want to know the ins and outs of the hiring process.

The article is split into 7 sections, based loosely on the chronological order of the process and the logical sequence of the ideas, as follows:

  • Courses, projects, education
  • Cover letters
  • LinkedIn profile and CV content, format, and other tips
  • Interview
  • Salaries
  • Recruiters
  • Switching professions

Courses, projects, education

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Naturally, before you can look for a job, you must consider the skills you already have and where you’ve acquired them. This may come from a traditional university education, online courses, and bootcamps, and from simply learning on the job, which is usually what we mean when we say “experience”. So let’s start with a traditional education – how important is having a Master’s degree? Perhaps you only have a Bachelor’s degree level of education or maybe you don’t have any degree at all. How important is this factor to a recruiter?

Although Alicja believes that there isn’t that much difference between a Bachelor’s and a Master’s, there is a bit more distinction when it comes to having a Ph.D. For instance, it would be a must for a team that is very research-heavy, where maybe they aren’t working on a specific product. The role may require doing a lot of research and writing papers, so a Ph.D. would be a must in such cases. However, this is certainly not the majority and then a Bachelor’s or Master’s would be sufficient. In some cases, the degree may not be listed as a requirement at all, but an employer will want the candidate to have a solid education in terms of understanding the maths behind some of the tools they would be using and the algorithms. But this doesn’t mean that it absolutely has to be a Master’s degree.

So what about somebody having two Bachelor’s degrees? For example, one in IT and one in account management. Would this make a candidate stand out among others? As impressive as having two Bachelor’s is, Alicja doesn’t think that this would be something that tips the scales one way or the other. It’s important that there is a technical degree, whether it’s computer science-related or related to machine learning. But if there is also another Bachelor’s degree in a non-technical field at the same time, it is more of a quirk than an advantage. As always, this depends on the role, to be honest. If someone has account management experience, this naturally leads to assumptions like the person may be good at presenting and have soft skills like managing stakeholders. However, an assumption is not a concrete thing and thus doesn’t offer a significant advantage in and of itself.

With traditional education out of the way, there are plenty of other things a person can do to further their education. So do recruiters consider portfolio projects such as courses from Coursera when reviewing a potential candidate? Similar to the double Bachelor’s, Alicja believes that experience plays a greater role than courses. Experience means what specific models or tools you used and how you implemented them. A lot of people tend to do the Andrew Ng Coursera course – if this adds value and helps you, even if it’s just to pass the technical interviews and refresh your knowledge, then that’s great. But it is more of a “nice to have” rather than a must, unless, of course, it is listed as a requirement in the job description.

Generally speaking, when it comes to courses, pet projects, and other things that enriched your skills, it’s something you accomplished and something you did from beginning to end. That also says something about you – you committing to doing that and putting in those hours. Even if it may not be the thing that a recruiter or hiring manager will base their entire decision on, this type of work also adds value and makes a good addition to a CV.

Cover letters

As anyone who has spent a bit of time researching on the internet while looking for jobs, the question of the cover letter will inevitably come up. Is having a cover letter even important or should the focus be more on the CV?

If the cover letter is obligatory during the application process, and sometimes it will be – then, of course, it’s important. If it is mentioned as a must in the job description, that means that the recruiter or hiring manager, or both, will read it. With that in mind, a lot of recruiters will not read the cover letter because they simply do not have the time to do so, but that doesn’t mean that someone involved in the hiring process will not either. If the job is for an entry-level position, the importance of the cover letter raises substantially.

If you’re a graduate and there is a junior-level data scientist position available, then a cover letter would typically be something that the recruitment team would look at. This is primarily because you probably don’t have that much experience to put in your CV, so the team wants to understand your motivations a bit more than your actual experience. However, if it’s a more seasoned or a more senior-experience level position, it is advisable to omit the cover letter unless it is explicitly mentioned as mandatory.

LinkedIn profile and CV content, format, and other tips

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Many people, especially professionals, treat their LinkedIn profile quite similar to their CV. This makes sense considering that recruiters and hiring managers tend to look at both of these sources when reviewing a potential candidate. So what information do recruiters look for when scrolling through the potential candidate’s profile or CV and how are these resources different from one another?

CVs and LinkedIn are very similar because they are used to convey a person’s experience and education. A CV is only slightly different in the sense that on LinkedIn, there is a specific format that is set for all users, whereas a CV is more or less open-ended. Typically, the first things that recruiters and hiring managers see are the experience, the role, and the responsibilities, after the name and the picture, of course. This differs from a CV, where people tend to put their education first. However, regardless of the resource, experience is usually the first thing recruiters look at, and the education after that. Of course, this depends on whether you are a recent graduate who doesn’t have much experience, in which case it is better to highlight the education as a first point. With that in mind, the experience typically beats most of that. Even if it’s just a six-month internship, it’s already better to put that on top of the CV, because that’s what a recruiter would look for anyway.

On CVs, people often tend to put a title and a list of responsibilities, or just a list of tools. However, you must keep in mind that the person looking at this list can’t be sure what you did with the things you listed or your depth of knowledge on them. Another thing to avoid is an overuse of buzzwords, as this can make things very confusing for the person looking at your profile or CV, especially if you actually have no experience with the things you mentioned. To combat this, it would be a good idea to showcase the things you and your team actually worked on, what your role in the project(s) was, and what you accomplished. It’s all about finding a balance when it comes to the use of keywords and showcasing your actual skills.

Your responsibilities and what you did at your job should be very clear, as opposed to listing what the team did. Of course, this doesn’t mean “don’t give credit to the team,” but you should not own up to things that you didn’t do on your own. Instead, highlight what your part in that team effort was. This will help to drive interviews and be useful for the interviewers further down the line because they will be able to ask you more informed questions and avoid asking you totally irrelevant things, which could inevitably confuse you as well.

Other points may initially seem small from the outside but can be critical when a hiring manager or recruiter has to pick your CV over that of 100 other candidates. One such detail is to put the month and the year in the job history, and not just a year to year because that could be up to a 12-month difference sometimes. There are, of course, quite practical things like making sure there are no typos, but that’s quite obvious.

Some less obvious nuances are things that do not reflect your knowledge, skills, or experience, but instead, serve to add bias to the recruitment process. Things such as a photo and your birthday are better left off a resume if they are not a specific requirement of a potential employer. We are all biased, and it’s unconscious bias. Not intentionally, but when you see a photo first, people immediately start forming assumptions without even knowing it. Leaving these things off a resume also helps you as a candidate to reduce that bias, and hopefully be more successful.


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Alright, so you’ve got your CV, LinkedIn profile, and cover letter down pat. You’ve done a great job on all three counts and have been invited to an interview. So what does the hiring process look like and what are the typical steps at the interview stage? A lot of the time, of course, the overall process will depend on the company. But typically, especially in tech and data science, the steps are as follows:

Recruiter interview: This is often the first step. These interviews tend to be quite short, but this depends on many factors, such as the size of the company, the system they have in place, and on the recruiters themselves. This interview involves primarily soft-skill questions like what you’ve been doing over the course of your career, what your responsibilities and accomplishments were in your current or previous job, your education, etc. There may also be a

Technical screening: This is usually an hour-long interview with a data science interviewer, which involves many more technical questions.

The final round: typically involves physical on-site interviews, hence the name “on-sites”. Due to the pandemic, these have mostly become virtual but may go back to being physical as restrictions are lifted. The final run could be anything from two or three, all the way to five or seven interviews. Again, this depends on the company and how big they are.

For the majority of interviews, you are likely to be speaking to a person who is not technical, especially for the recruiter interview. Therefore, it’s important to keep in mind that it’s not just about you proving that you have the technical ability and skills, but that you are also able to explain those complex concepts to people who have no idea about machine learning.

Be prepared for behavioral questions as well, such as “Can give me an example of a situation when you had to work with a difficult stakeholder? Why was it difficult?” A lot of people tend to answer in very general or hypothetical scenarios, so try to answer by giving an actual example from the past and walk the interviewer through your actions and what you did in that situation specifically. What was the outcome and what was your role in it? The purpose of these behavioral questions is to see if you fit in with the culture of the company and the values that the company has.

Aside from behavior, there are also likely to be practical questions. This will typically be about the notice period – if you’re currently employed, how soon you could start if you decided to join us, and, obviously, salary. What are your salary expectations and how active you are interviewing currently?

Naturally, this process may sometimes not be as organized or as structured as what is listed here. After the interviews, there is another stage where the decision is made. If the decision is positive (the company wants to hire you) the company then makes an offer and talks about the conditions. This most likely comes in the form of a contract and then the candidate is onboarded.


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When it comes to salary expectation questions, you may have heard the advice – “Never say the number first.” So is this good advice and should you play this game of ping-pong with the recruiter or hiring manager when it comes down to negotiating a salary? Essentially this can be broken down into the following points:

  • Your current salary
  • Your expectations
  • The company’s salary structure / Negotiations

Your current salary

If the recruiter asks about your current salary, you should know that it’s not necessary for you to respond. In most European countries, at least, your future employer won’t be able to check your actual salary because it’s personal data, which is protected by privacy laws. Thus, you can ignore that question or just say, “Look, that’s not something that I’m willing to share.” Sometimes recruiters are told by their bosses that this is a question they have to ask and thus, they will ask you, but just know that you are not obligated to answer.

Your expectations

If you ask the recruiter to give a number first, you may be met with a response such as “It would be super helpful to know the expectations.” This is an honest question since the recruiters want to know if your expectations are completely off from what they can offer you. If this is the case, then you probably shouldn’t be wasting each other’s time. However, you can also counteract this by saying “This is my initial estimate of what I want, but this may change because I’m interviewing with other companies. I will also do my research.” Recruiters typically will expect that you are applying to more than one company.

In the data science recruitment market, it would be strange to expect that people will not have other offers. If you’re just a graduate, it may be harder to find a job, but people change their minds and expectations change and that’s fine. As long as you’re open about this from the beginning and say, “This may change,” and keep the recruiter posted – they will not hold it against you. Just let us know so that we can counteract and try to still maybe do something about it.

When setting expectations, it is a good idea to do some research about the typical salary for the role that you’re applying for. The resource Glassdoor, among others, allows you to see the salaries of people from the same company you’re interviewing with. If you set expectations that are backed by research – checking salaries for the same role globally, for different companies, and setting a range – you become informed of the market and have a much higher chance of achieving success.

The company’s salary structure / Negotiations

For the majority of companies, the way they approach salaries can be divided into one of two categories: either they have a set range for roles (typically this approach is used by bigger companies) or it all depends on what you negotiate with the person that’s hiring you.

In either case, it’s good to ask “How does your salary structure work? How does the leveling and seniority tier work in this company?” before you answer the question of salary expectations. If it is the type of company where it all depends on how well you interview and what you ask for, then don’t tell them your expectations. However, in a lot of companies, especially the bigger ones, there is a level system in place. That means that to every level, whether it’s Junior, Mid-level, Senior, or above – there is a band attached. In this case, no matter what you’re going to say, even if the number is very low, you will be told what that specific band is. A lot of companies try to avoid giving a salary that is out of that range if you truly belong to that tier, even if you’ve given a lower number, as it leads to a weird atmosphere between the members. If the company has established ranges and leveling, then it’s okay for you to share your expectations. You don’t have to actually say a specific number as a range will usually suffice.

If the company does not have a salary level structure in place, your salary will likely depend on how good you are at negotiating, which is typically the case in smaller companies. This is where your salary research will come in handy as you will be less likely to have overly high or low expectations. Generally, if you set a price that’s too low, you will likely be stuck with it, given that you accept the position. However, the tendency for data professions, according to Alicja, is that they don’t have a huge gap between what the company can offer and their expectations.

Interacting with recruiters

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If you want to impress a recruiter, it is best to try to answer their questions as much as possible. Try to think about the fact that you’re speaking to someone who doesn’t understand much about your profession in the technical sense. Although a lot of recruiters do try to understand machine learning and can sometimes tell the difference between certain aspects, just try to explain as much as possible in words that are maybe not as technical.

You may often be met with a question that is more open-ended, in which case, try to avoid giving a simple ‘yes’ or ‘no’ because the interviewer’s goal is to understand your motivation. They want to know why you were doing this particular thing or what it was that you even did. Do your best to come prepared for the interview. This doesn’t necessarily mean you have to know everything about the company where you’re interviewing, but be prepared and have some specific examples.

The behavioral questions you are typically asked do not vary that much and are usually about similar values. Companies want to know if someone is a team player, so the questions will likely be around that. If you were more in a senior role and leadership, then you may get asked something like “How did you lead the team? How big was it? Can you give me an example of how you would grow someone?” So try to be ahead of that and try to think about it before attending the interview. The interviewer will probably ask you questions that need specific examples, so try to come up with some specific examples as opposed to hypothetical scenarios.

Coming prepared for the interview with your own questions is also sure to impress a recruiter. If you’re motivated and you’re actually interested in joining the company that recruiters are representing, then ask questions about the company or the culture. If the person being interviewed is not asking those questions, the recruiter may question whether they are actually interested in the company or just attending the interview as a backup plan.

Commitment is a sticking point for recruiters. If things change, just explain what happened, whether it be salary expectations or something else. The name of the game is respecting people’s time. Albeit recruiters can also be quite difficult about this or not very good at keeping you posted and you never hear from them after you were rejected. But if you’re interviewing with many companies and some company makes you an offer, let the recruiter know as soon as possible. Be open with the recruiter about your intentions.

Switching professions

There are many situations in life when it’s time for a change. The change can vary greatly, from simply switching companies to choosing a completely different field of interest. So how can career changers achieve success when searching for a data profession?

A lot of companies want candidates to have a specific degree, even if it’s just a Bachelor’s – but they do want one from a university and in something related to computer science. If you come from a different background and are changing careers from something not tech-related, it can be quite challenging. Although it is not uncommon for people to take some Coursera courses and then try to swap, this can prove very difficult because experience is what matters the most for recruiters.

Therefore, the best advice is to try to gather experience, even if it’s an unpaid internship or apprenticeship. Of course, it’s not easy to just do an internship that is unpaid for six months because you have bills to pay, but this is where the challenge comes in. Try to network as much as possible with people who are data scientists in the companies you’re interested in and connect with recruiters on LinkedIn. This can be in the form of meetups or any kind of community you can find.

Then there are also headhunters from recruitment agencies who may typically have worked with many different clients. They work with many different companies and have a wider net, so they may have some opportunities somewhere to offer you. The more recruiters you connect with, the better.

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All in all, while being a data professional can be a rewarding career and the job hunt rigorous, the main thing is that you are happy with what you do. Armed with the knowledge you have gathered here, you can proceed further and establish a good foundation that will prepare you for any interview that you come across. Be sure to know what you want, learn how to express these needs, and do not be afraid to voice your concerns. I am certain that you can succeed – you’ve already taken the first step!

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