SDS 077: Finding the right Data Science Company that best fits you

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Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.

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Hello and welcome back to the SuperDataScience podcast. 

Super excited to have you on board, and today we’ve got a special guest. Today we’ve got an analytics and data science recruiter, or headhunter, Richard Downes. So Richard is the Founder and CEO of Stirling Global, it’s his own recruitment company, and he’s helped dozens, if not hundreds, of people get jobs in the space of data science. If you just look at his LinkedIn profile, you’ll see over 45 recommendations from people who he has helped in the space of data science recruiting.

And that is exactly what we’re going to be talking about today, how to get a job in data science. Or more importantly, how to go about finding a job and setting up your CV, setting up your LinkedIn profile, going to the interview – what are the things to look out for in that interview? What are the questions you might be asked, what are the questions you should be asking? And what are the type of companies you should be looking into. And just generally, where the space of data science and analytics is going.

So if you’re looking for a job in data science, or if you are looking to hire somebody in the space of data science, or even if you are just open to new opportunities, and maybe some day in the future, you might be looking for a job, this podcast is for you and you will get some very valuable tips out of here.

So I’m very excited about this episode, and without further ado, I bring to you Richard Downes from Stirling Global.

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Welcome everybody to the SuperDataScience podcast. Today I’ve got a very exciting guest, CEO and Founder of Stirling Global, Richard Downes, calling from London. Richard, welcome to the podcast. How are you going today?

Richard: I’m very good, Kirill. Thank you very much for having me on, I really appreciate it.

Kirill: It’s great to have you on, we’re going to have some amazing conversations. And to start off, how’s the weather in London today?

Richard: I think you know the answer to this question. It was good yesterday. That’s as good as we can – we can name the days in history that we’ve had good weather over here, but hopefully it’ll improve. Hopefully in August we’ll get at least one day of sunshine.

Kirill: Hopefully. Well I’m actually coming over to London tomorrow. I’m in Paris right now, coming to London tomorrow, and hopefully I’ll bring some sunshine. I’ve been carrying it around from Australia and handing it out to different cities. So I’ll try my best.

Richard: You’re obviously a sadist, Kirill, if you’re coming over from Australia to London. But good luck with that!

Kirill: Okay, Richard, tell us your story. For those listening who don’t know, Richard is a recruiter in the space of analytics. What a surprise, what a coincidence that you are on the show, such an exciting thing. Tell us your story. How did you get into this space?

Richard: I’ve been in recruitment since 2001. I started in recruitment in general at the tail end of the GSM boom of telecommunications, but slowly but surely found my way into this space over the course of the last 5 years, I guess. I went from business intelligence recruitment into the analytics and data science space. It’s an industry that’s always fascinated me and I think it’s only become more interesting as the years go on, especially now with artificial intelligence, machine learning, and other really cognitive computing. I find it a very fascinating area. I’m not going to humiliate myself by my lack of knowledge technically, but obviously we’re going to focus on how best to help your listeners in terms of how best they market themselves, but also about going and getting the job that they’re looking for and where they want to go with their careers, right?

Kirill: Yeah, totally. I think that’s a fantastic way to approach it. I probably wanted to start off with something that you mentioned right before the podcast. There’s a huge disconnect, a disconnect between what type of data scientists companies want and what type of roles data scientists are looking for in the companies that they want to work for. Can you comment on that? I think you made some very interesting comments on that before.

Richard: I think there’s obviously two sides to this coin. The first problem is that there are a large number of companies out there that are, shall we say, unrealistic with what they expect a data scientist to be able to do. So they have a wish list that can include some “quite interesting things”, shall we say, that they’re expecting a data scientist to do, from Java programming through to predictive analytics, machine learning, they also want experience on Hadoop as well. So, I think that the challenge at the moment is finding companies that are realistic with their expectations of what people can do.

And then from the candidate side, I guess from your listeners’ side, is finding those positions that are going to be most interesting and most engaging for them, because I think a lot of people get into jobs where they go through the interview process, it’s gone great, they’ve spoken to the interviewer, and then they get in the job and 6 to 12 months later, or 18 months later, they’re sitting there at their desk thinking, “This is isn’t what I expected it to be.” So I think a lot of this comes down to how people can approach how they go about speaking to their interviewer, and doing their research before signing that contract.

And that’s how I help people, and that’s what I’m most passionate about, because there’s nothing worse than — I don’t know if you’ve had this experience, but there’s nothing worse than being on a job that you can’t stand, and feeling like, “Hey, this isn’t what I signed up for.” So that’s the disconnect, it’s on two sides. And I’m really passionate about working with companies to manage their expectations, but also with people to help them best ask the kinds of questions that although they might be uncomfortable, will save them many tears later on down the road.

Kirill: That’s very interesting. I totally agree with that. I personally haven’t been in a situation where I’m not passionate about what I do. I’ve been in a situation where I join a company, I really love what I do, I do it to full extreme, I’m putting 110% in, and then just over time I grow to a level where the company cannot help me go further and at that point I’m like, “Okay, yeah, that’s kind of the limit. I’ve reached my limit of growth here.” And from then on, then I’m not interested. I used to get disinterested. But I totally can attest to what you’re saying, that people should look for opportunities, not just to get a job, not just to be employed, but also be happily employed, have a job that fulfils and helps them grow, even if it’s for a limited time. Like, ideally, you want a job where you’ll grow for 5-10 years, but even if you’re going to grow for a year or two, you want that fulfilment from what you’re doing.

And it’s really exciting to hear that that is something that you focus on in your recruitment, because I know a lot of recruiters out there, their goal is just to put the person in the position and move on. And even though that works for a short time, it’s not the way to build a reputation in the industry. So it’s great to hear that this is something you focus on. In your five years that you’ve moved into the space of data science, can you give us some statistics, some numbers around how many people you’ve placed in roles, or maybe some examples or something about your past work?

Richard: Sure. I mean, I can’t go into too many specifics. I think what I would say is, at the end of this podcast, obviously, if you want to share the link for my LinkedIn profile, there’s over 40 recommendations on there from people that I’ve supported in one way or another. I’ve recruited in Europe predominantly, that’s been the bulk load of my work, but recently I’ve been doing a lot more in the U.S. I was focusing a lot more on consumer insight, shopper insight, because I think this retail piece is particularly interesting to me at the moment, and it has been.

However, I’ve moved more across to supporting start-ups, solid venture capital/private equity-backed start-ups. The reason being is that these companies sometimes struggle to get through all the noise of advertising for good quality people and I can work with them. The other thing is, I like working directly with key decision-makers, which is another thing we’ll touch upon for the benefit of your listeners. One of the major downsides when you apply for a job, especially online, is that you send your CV and often it can sometimes be just like Alice in Wonderland, just sending it down a hole and you don’t know where it’s going to end up, who’s going to read it, and if that person is going to understand your CV, or your resume.

So, yeah, going back to your original question, I’ve recruited worldwide, I’ve worked with smaller start-ups all the way through to multinational corporations. But, obviously being a small company, I have to pick and choose my battles so I typically tend to work with companies that are going to work with me and have that relationship whereby they’re going to collaborate with me to hear why candidates should be considered. There are a lot of people that on paper don’t necessarily look like perfect applicants, but they have attributes that perhaps they’re not able to get that across on paper or in an interview. That’s where I come in. That’s where I work with my clients quite extensively. I can’t give you any stats, I can’t name any company names, unfortunately.

Kirill: No, that’s totally cool.

Richard: Does that give you an overview?

Kirill: Yeah, yeah. And for those who are more interested, we’ll share your LinkedIn on the show notes and they can definitely check out your recommendations. That’s a fantastic way to learn more about the work you’ve done. But I just wanted to touch on the point that you mentioned, that sometimes on paper people might not look like the right candidate for a role, and it is the role of the recruiter to determine that and then help those people into the role. I know that from my personal experience. I wanted to leave Deloitte and I wanted to move on. I applied to different roles, and there were some banks I was interviewing with in Sydney, but then I really wanted a job still in Brisbane, where I live in Australia, because I didn’t want to move to Sydney.

And then one recruiter contacted me on LinkedIn and there was a position for a—it was kind of like an insight specialist, but basically a data scientist in a pension fund, where I ended up eventually, and the requirement there was 6 years of experience, and I only had 4 years of experience or something about that. If the company went and looked at my profile just based on that criteria, just on that hard criteria, because they go through hundreds of candidates every day, they don’t have the time and they just go, “Oh, not enough experience. That’s it, done. We’re not going to interview this person.”

But because he spent the time, and he looked at my profile, and then he actually requested a meeting with me to ask me more about my experience, even before completely revealing what role it was that he was recruiting for and what company it was with, he first checked me out to understand what my knowledge is and if he can convince the company that even with four years of experience, I would still be successful in this role just as a person with 6 years would have or even better, and then after that meeting he went and did convince them. That I really appreciated. Huge shout-out and thank you to that recruiter for helping me with that. And I think that’s an important role of recruiters. What do you say to that? Do you think that recruiters should focus more on that?

Richard: I think the challenge for recruiters – and I’m not here as a spokesman of the recruitment industry – but one of the challenges that many recruiters have is that they’re put under a great deal of pressure. I mean, I work for myself, so the only person that puts any pressure on me is me, and obviously that gives me a great deal of freedom to decide who I choose to work with, who I choose not to work with. There’s a lot of people out there, recruiters that are just trying to get by, and they have a great deal of pressure on them from above. I don’t want to speak on their behalf, because I don’t know how they go about their day, but what I would say, rewinding back to how we can sort of focus on helping your listeners, there’s something that was quite interesting that you said a second ago. You mentioned that job was a hedge fund, was it?

Kirill: No, it was a pension fund.

Richard: Pension fund. So, when your resume was sent, who was it sent to from the recruiter? Not the name of the person, but their position.

Kirill: It was an HR specialist, I think.

Richard: Okay, so what typically tends to happen, that I see loads, the problem within many HR departments is that they have a difficult job because they’re recruiting across every single different type of position within the company. And the challenge that that presents is that in a lot of cases they don’t understand the specific technicalities of what value someone can bring. I like to work directly with the decision maker, whether that’s the Head of Analytics, Director of Analytics, Chief Analytics Officer, Head of Data Science, whoever that might be. You can then explain and justify why that person should consider that particular candidate.

The issue is, with many HR departments, is they’re time-staffed, so the challenge that they have is they have to screen a CV and they haven’t got the opportunity to speak to every single candidate in the way that I have. And that’s where a lot of people get really frustrated. I don’t know whether you see this on LinkedIn, but I see some people and they’re like, “What’s with this data shortage? I’ve sent my CV or resume to a hundred people and I’ve had no response.” What I’m most passionate about is, when people compose their CV, they need to compose their CV for the benefit of the person that’s going to be reading that CV. It’s not a composition of everything—for a lot of people it’s like a brain dump.

Kirill: Yeah, like 5 pages.

Richard: Exactly. And the CV, a modern CV now, it needs to be looked upon as a marketing document. That’s what it is. It needs to capture the attention of the reader as quickly as possible, justify why that person should be considered, and what benefit they’re going to bring to that organization. And that’s another disconnect because the person reading the CV is reading it to see what value that person can bring and the person that’s sending it basically just thinks in a lot of cases, “I’m going to put everything down and hope for the best.” That’s really not the best approach.

Kirill: Yeah. It’s great that you touched on that. Let’s talk more about that. What are some of the best practices that our listeners can employ for their CVs?

Richard: The first thing I would always recommend is research who you’re sending your resume to. That’s a great starting point. I personally would send the CV directly to the hiring manager if I was in your listeners’ point of view, at least connect with them on LinkedIn.

Kirill: Just to clarify, the hiring manager is not HR, it’s the person that you will be working for.

Richard: Yeah. I’m not talking about circumventing hiring processes, I’m not saying that, but it can be quite useful to connect with the hiring manager. Look at the sort of people they hire, look at the sort of people that they have within their team. Do the majority of their team have master’s degrees, are they PhDs, did they graduate in a certain field, do they have experience in a certain industry? Look at the patterns that are there. And then you know that those specific things are important because obviously those people have been hired, so they’re success stories. So when you’re composing your CV, you need to focus on the things that you have in common with the people that are already working there, if that makes sense.

Kirill: It makes sense. And highlight those things in your CV?

Richard: Yeah. And also be prepared to focus during the interview on the things that you think are going to be important to that hiring manager. I get a lot of feedback from hiring managers and also from HR professionals that the person has gone off on a really technical tangent which, whilst it’s really important to have that passion, in a lot of cases – especially when you’re talking to someone like myself, for example, or even HR – they’re not as educated in that particular area, so you have to almost break it down to what the value would be to that particular person, if that makes sense.

Kirill: It makes sense. And we’ll get to interviews in a second. Let’s talk a bit more about CVs. I think there’s a bit more we can explore here. So, from your experience, what is the optimal number of pages for a data science role that a person should have on their CV? Should it be one page or two or three? Like, it’s a trade-off: the readability versus amount of things you can convey about yourself.

Richard: I think three pages is the maximum you should go to. I mean, I personally think that from university, at the end of your university, then your experience should show. If you don’t have experience professionally, and you just have academic experience, it’s really important to have a good summary on your CV. So, specific things that you worked on in your studies, things that maybe you’ve published, maybe conferences that you attended, how you invested in yourself, just really a solid summary if you’re a recent graduate.

If you’re someone who has been working for a number of years, try and make it really detailed as to what you’ve been doing within the last 10, and then past that, you don’t need to elaborate much because it’s quite far in the past.

Kirill: All right. And what do you think of one-page CVs? I remember when I was applying, I was applying for three companies at the same time, so for every company I would have a set of a one-page CV that I sent to the recruiters, and then I think it was a two-page CV which I would bring to the interview with more details. So, my one page would be something that they can look at and they don’t have to scroll through it or turn the pages, they can just see everything in one glance and it’s very nicely formatted. Do you think that’s a good approach or that’s not the best way to sell yourself?

Richard: No, I think it’s down to the position that you’re applying for. Did it work? That’s what I would ask you, Kirill. Did your approach work?

Kirill: I found that it’s a good way to get the attention. A lot of the time, people have a thousand CVs to go through in a week and they don’t have time to even go to page 2, they only look at page 1 anyway. So I condensed everything as much as I could, put it all on the first page, and when they looked at it they’re like, “Oh, this guy is cool, we’ll shortlist him.” And then once I got the phone call for the interview or the phone interview or whatever, or the recruiter decided that now we can proceed further, then I sent them the more detailed CV.

Richard: Yeah, I mean if it worked – and obviously there’s definite clues in there – I would say one-page CVs are fantastic if it’s laid out nicely and clearly. I do think, unfortunately, where we’re all so time-staffed with respect to our days, that it is important to simplify things for the benefit of the person that’s actually reading that particular document. So, yeah, your experience is it worked. I’ve seen one-page CVs that are good, and I’ve also seen one-page CVs that are not so good. I think one of the things—it’s actually a very interesting point that you make—a really important thing now is grammar and spelling, so with things like Grammarly now, there’s no real excuse for not making sure that that’s at least checked.

Now, obviously there might be an odd mistake here and there, and obviously the person reading it will forgive that, but you want to make sure that literally there is the minimum possible chance that the person reading it is going to have a negative reaction to it, and spelling and grammar is the minimum thing that you need to ensure. So by all means, check it on Grammarly, send it to someone that you trust that can check it for you, even maybe outsource it to someone on fiverr.com.

Kirill: Exactly, I was about to say Fiverr. For $5 somebody will spell-check your whole resume.

Richard: Exactly. That’s what I would do. But yeah, with regards to the CV comment that you made, key thing is to simplify it, have a good summary and convey, or articulate if you will, what the benefit is to that particular person and organization about hiring you. That is the purpose of the document. It is not for you to give a load of information that’s potentially not important to that person. You have to go at it from the angle of “That person is time-staffed.” You need to be able to articulate it quickly to give them the best possible chance of reading it. It’s for your benefit that you’re doing it for their benefit, if that makes sense.

Kirill: Yeah, it makes sense. And what are your thoughts on—like, I’ve seen a recent trend in colourful CVs with images or bar charts of how a person’s skills stack up. It’s really nicely presented, it’s like an infographic about the person. What is your thought on that?

Richard: I think anything that captures someone’s attention—I think this is the problem that we’re all having, right, Kirill? Nowadays we’re so bombarded with information that it’s really important that you catch the person’s eye. Do I think that it’s going to work every time? No. Sometimes the best things that I’ve seen are very clean, neat CVs. I personally like that. That’s my preference because I don’t like loads of stuff, loads of information. I just want to get straight to the point and see it and that’s how I would approach it.

But yeah, I’ve seen some awesome CVs where people have spent obviously a very long time doing infographics and they look really cool, but I would be inclined to do a very neat, clean, almost minimalist CV and really work on your preparation as to how you’re going to speak to that person and prepare your questions for the interview, do your research really, really well with regards to that company, look at the sort of people they employ. That’s where I would invest my time. CVs are important, they’re great. There’s a question mark about whether they’re as important as a LinkedIn profile, which we can go into if you’d like.

Kirill: Yeah, let’s move onto that. Thank you for the tips on the CVs. Let’s move on to the LinkedIn profile.

Richard: With respect to LinkedIn profile, that is a CV. I think you have to look at it in that way. When people are searching you, it’s got to have all of the aspects about what you’re doing that you’re passionate about but that you’re most experienced in. One of the main things is to be honest about your experience as well. Don’t just lump on a load of skills if you’re not skilled in that area.

I personally think narrowing it down to two or three things that you’re really good at is a better approach than having 30-40 things on there because if someone is reading it, they want to get straight to the point of what the value is of that person and what that value is going to be to that organization. That’s my biggest advice with regards to LinkedIn, is try and share interesting content, engage with your connections as much as possible, but try and put together a LinkedIn profile that is extensive and showcases your experience in the same ways you would a CV.

Kirill: Exactly. We’ve already mentioned with other guests on the podcast how important LinkedIn is. I’d just like to reiterate here that personally for me that made a huge difference. I remember when I was back in Deloitte, I don’t think I even had a LinkedIn profile and then someone said, “Oh, you should get a LinkedIn profile.” I put one together and then even before I was looking for a job, while I was still happy at Deloitte, I set myself a goal, “Okay, I’m going to improve my LinkedIn.” And then when I started looking for a job, I really focused on LinkedIn. I spent about six months constantly working on improving it and researching other people’s LinkedIn profiles, getting my skills upvoted and so on, and then when I started sharing content, that’s how the recruiters started contacting me. That’s where I actually found my next job, through a recruiter contacting me through a piece of content I shared.

Richard, you can comment on this, but you don’t have to write content yourself every time; you could, but you can just find interesting articles online and then share them on LinkedIn. But at the top, when you’re sharing, you can put a two or three sentence summary of the article, your thoughts on the article and say why you liked it and share it. People will appreciate that and recruiters will see that you’re sharing content with others even though it’s not your original content, you’re just commenting on it. That’s personally what I did. Do you find that a valid approach, Richard?

Richard: Yeah, for sure. I mean, I think the most important thing is to be genuine in your approach. I’ll be honest, I was guilty of this when I first started promoting myself and my business. I was probably promoting a quite rigid thing of being a recruiter and “I can do this, I can do that and I’m very serious,” you know. I think you have to play to your strengths, just be yourself, and I think that’s one of the things with social media, one of the downsides is that we adjust our personality for how we communicate via social media so you end up being almost two, three different people. Do you know what I mean? You meet someone face to face and you’re one person, you’re own social media and you’re another.

And one of the main problems with social media is that once it’s out there, you’ve put it out there. People are very conscious now of what they say. But when you’re talking about data science and analytics and things that you’re passionate about, I think it’s really important if you have an opinion about something and you want to make a comment about it – share it, as long as it’s nothing too controversial – we’re talking about analytics and data science, so there’s not too many places – I would steer away from politics for sure. I made that mistake once and I won’t make it again.

Yeah, it’s so important to share, so important to connect with different people. You’re in Australia, I’m in London. That wouldn’t have been possible 15-20 years ago, for us to communicate in this way. But be genuine and ask for help from people. It’s amazing how helpful people are. I used to really cynical about this. I used to think, “Well, people are just going to be too busy. They’re not going to be interested.” It couldn’t have been further from the truth. Most people are really good people and genuine people and they want to help.

And I’ve been lucky enough to interview people on my blog, people like Tom Davenport, Jill Dyche of SAS, Bill Schmarzo down at Dell EMC, people that I never expected that I would be able to communicate with and they were really kind to give me their time. I think a lot of times, Kirill, this might be for the benefit of your listeners: Just ask. Ask for help. And 9 times out of 10, the person will respond. It might not be immediately, but they will respond and I think that’s what’s awesome about LinkedIn now and social media in general. You can connect with pretty much anyone that you want to. And if you can’t, there will always be someone else that you can connect with. So, really, there’s no real excuses.

Kirill: Yeah. And I actually heard that it’s why people who you usually don’t expect to hear back from, why they do reply often, is because it’s actually lonely up at the top. When people achieve success and they build a name for themselves, it’s like they want to give back. They have this feeling they’re successful, they’ve done so much, created all these things, but at the same time they have a feeling to contribute back to the world. Whenever there’s an opportunity, if they have the time (of course sometimes they don’t have the time), but if they have the time, they’re very open to doing that.

I’ve heard that many times and sometimes I’ve found that myself. I connected with a data science thought leader Ben Taylor. Before, I used to always think, “Wow, it would be so cool to chat to Ben Taylor one day,” and now he’s already been a guest on the podcast and he’s come on the podcast a second time. It’s great what LinkedIn can do, I can totally agree to that.

Richard: Yeah, and I think that some of the people that I’ve been lucky enough to speak to, not just within this industry but other industries, just through reaching out—I won’t name the person, but I saw the documentary on Netflix and I thought it was an awesome documentary and I reached out to the director of the documentary and I had a 45-minute conference call with that person just through asking. I wouldn’t have even thought to ask and I did. Now, obviously he might not have responded, but he did. So, I think a lot of times you have to go against that initial reaction – and this is applying it to looking for a job.

The initial reaction of many people would be, “I don’t want to ask that person because it makes me feel uncomfortable and I don’t want to intrude and I don’t want to send them a LinkedIn invitation because I don’t know them.” Sure, if you send them a LinkedIn invitation and don’t introduce who you are, then it’s not good. But if you introduce yourself and if you’re open and you’re prepared to take a chance, you’d be amazed with the results that can happen. I think sometimes people are a bit too within themselves. They need to push themselves a little bit more to do uncomfortable things if they want a different result.

Kirill: Yeah. And what you mentioned before is also very important. Just be genuine. Like, if you don’t pretend and you just message someone and say, “Hey, I’d really like to chat to you about this and some thoughts I’m having,” you’re genuine then you have nothing to worry about. Yes, if they reply, then you have some great conversation. If they don’t have time and they don’t reply, no worries. There are some other people that you can talk to. So, yeah, being genuine I think is also very important in all of that.

Richard: And I think, going back to looking for a job, I think it’s really important that you’re genuine because the person that is looking to hire you, they have to match who you are, they have to respect who you are. There has to be an alignment of their values and your values. Sure it’s a transaction, sure you’re getting a job, sure it’s about working for that company and them paying you and you getting paid, but it’s more than that. I really do firmly believe a job—you spend so much time there that there has to be some connection there with both your manager and your colleagues because without that, what’s keeping you there?

Kirill: Exactly. You have to be there for the right reasons. Okay, we’ve talked about CV, we’ve talked about LinkedIn. We’re kind of going through the journey of somebody looking for a job in data science. And now they get to the stage of the interview. So, what do they do with it? How do they prepare? Do you have any tips? What do you wear? What do you say? How do you behave at an interview?

Richard: There are some golden rules, obviously, with regards to how you approach it. The first thing is do your research. In the same way that I speak with companies that are hiring, and I say the same to them, they need to be well prepared. Especially with experienced data scientists. If you’re going into a meeting and a person is sort of rushed for time, they haven’t read your CV – that’s a no-no, companies need to be aware of that person. If they’ve read your CV—you’d be amazed how many people don’t even read the CV, which is awful. Those companies should probably be avoided if they haven’t got the time to invest in reading your CV.

But how someone should prepare? First thing is research. Research the company, research the person that’s going to be meeting with you, look at their LinkedIn profile again, where did they study, what groups are they involved with, where have they spoken at, what conferences have they spoken at, maybe try and find one of their presentations on LinkedIn or another forum. Research about them and what’s important to them.

And also, if you’re going in for an interview, you need to prepare questions about the position and also about the company so that you don’t come away from that interview thinking, “Oh, I wish I had asked that.” The key thing is to invest as much time in the interview, in preparation, as you would as if you were actually in the interview, if that makes sense. So if it’s an hour interview, you need to invest an hour in terms of the preparation. Otherwise it can end up a bit of a vague conversation. You want to be asking things like, “What would I potentially be doing on a day-to-day basis? What is your definition of a data scientist? What do you expect your data scientists to do? What kind of projects are you working on at the moment? Who is your best data scientist? I’m looking at improving myself and my skills. What makes them the best? Who are people that you’ve had here before that haven’t worked out? Why didn’t they work out?”

So a lot of these questions you need to prepare in advance. It’s very easy for me to say all of this because that’s my job and I have to do that, but it’s really important in terms of asking. I put a post out on LinkedIn today, which was about when companies say, “We invest in personal training and development,” a lot of people just say, “Okay, great.” It’s really important, if that’s important to you, to ask.

Kirill: Yeah, that’s what I was about to say. You have to ask, “How am I going to grow in your company?” Because if you’re not growing you get bored and that’s not good for anybody, it’s not good for them, it’s not good for you. It’s important to understand that.

Richard: Yeah. And also if they’re saying that they’re going to invest in training and development, you need to go into the specifics, you know, “What kind of allowance do you give people monthly or yearly to invest in their personal training and development? Do you have an internal program, or do you send people to courses and seminars?” I’ve seen training and development be anything from they invite a speaker in once a year – they think that’s personal training – or companies are really committed to it and they send their people to external courses, they give them an allowance, they encourage them to—

Kirill: They buy them books.

Richard: Yeah. I’ve seen loads of companies be really committed and not be as committed. That’s why it’s really important that candidates ask specific questions about specific things that they want information about. Otherwise, as we said at the beginning of the call, you can end up crying about it further down the road because you didn’t ask the questions at the very beginning of the process.

Kirill: Okay. That’s some very good tips and I totally agree that people should ask questions. It not only shows the recruiter or the hiring manager that you’re actually proactive, you’re also finding something that’s good for you, but it also helps you understand better. Let’s move on to some of the questions. There are two types of questions that are asked in interviews: there’s technical or questions about the role, and there’s behavioural questions. Let’s start with some of the technical questions. Could you reveal some of the most common or most interesting technical questions you’ve come across in the space of data science?

Richard: The technical questions are depending on the job description in question. For example, many of the technical questions I would not be competent enough to be able to answer them, however I will qualify candidates. So when I speak to a candidate initially, I have a discussion with them about what’s important to them, where they want to go with their career. What I see a lot of companies doing, and it’s a lot more prevalent now, is they will send a test case before they’ve even interviewed the person. I don’t agree with that, by the way, but I agree with it after the first interview. We’re talking about people in a large number of cases that are in demand. If you send a test case at the beginning – and I don’t know about you, Kirill, but if every company out there wanted you to perform a test case, would you have the time to do it?

Kirill: No, of course not. I would look at the companies that first talked to me, and then we see that there’s some connection that’s clicking and then only would I invest my time into the test case.

Richard: Yeah. You mentioned the behavioural questions, yeah?

Kirill: Yeah, let’s focus on behavioural then.

Richard: So what specifically would you like to know?

Kirill: I remember I’ve been asked behavioural questions. These are things like, “Have you ever worked in a team?” or “What’s your biggest strength?” and then the next one is “What’s your biggest weakness?” and stuff like that. So, how to prepare for technical questions, and how to go about answering them? Maybe there are some specifics in the space of data science. Since I’ve had my last interview, the world’s moved on quite a bit. So what are the most recent trends in this space?

Richard: Well, from a technical perspective, you can prepare from a job description, because when you look at a job description, it pretty much gives you a very clear indication as to where the conversation is going to go. I mean, if you’re going via a recruiter, a good question to ask the recruiter is, “What are the most important things technically that your client is looking for?” Because if you ask that question, they might turn around and say they are really interested in your maths background, they want to know about your problem solving experience, they want to know how much experience you’ve had with R or Python. You’ll be able to concentrate the conversation on the area that’s most important.

So, the job description gives clues. Going back onto what I said earlier about LinkedIn profile, you can see on the LinkedIn profile from the other people that work at that company what that employer is going to be looking for because there will be trends in people that they’ve already hired, if that makes sense.

Behavioural questions—you’ve actually done quite well there, Kirill, because they are some of the worst behavioural interview questions. They always ask these questions. “What’s you biggest strength? What’s your biggest weakness?” I always say to companies, “You’ve got to be better than that.” Some of the questions that they should be asking are, “What’s important to you? What kind of data scientist are you? Where do you specialize? What are you most passionate about? Why are you involved in the data science industry? Where do you see the industry going in the future?” Companies need to improve in that area in terms of how they’re engaging with candidates because otherwise they’ll send them to sleep, like I probably am now to your listeners.

Kirill: (Laughs) No, no, no. I probably want to challenge you a little bit on that because I still think that questions such as, “Tell us about a situation where you had a conflict in the workplace and how you went about resolving it,” it’s not really related to data science and what I’m passionate about, but at the same time I think it’s important for both parties to have the answer to that. It’s important for the hiring manager to know how this person has resolved conflict in the past and what has caused this conflict in the workplace so they can kind of anticipate what kind of person they are.

Also, it’s important for the person that’s interviewing to explain these examples, to clarify in their own head why there was a conflict. It’s important if it’s already clear in their head that there was a conflict and how they went about resolving it. What do you think about those types of questions, where it’s not really related to data science and passion, but rather their work with colleagues and how they interact with others in the workplace? Do you think those are fair questions?

Richard: I think that’s a good question. I mean, I’m not talking about questions like that because that is a decent question. My issue is with the questions that are—I did a post about this last week, and I’m not going to keep referring to posts that I’ve done because probably no one reads them anyway, but with regards to the post I’ve put out last week, when you’re interviewing candidates, if it’s obvious that you have not read their CV and you open the conversation with “So you work for blah-blah-blah? Tell me a little bit about what you’re doing,” that’s a poor question.

The reason why it’s a poor question is because it’s obvious that you have not read the CV, you haven’t prepared as an interviewer to speak to that person. And I know that we’re all busy, but I think that a little bit of research from a candidate perspective, but also from a client perspective, and also from a recruiter perspective, where I have an obligation to the person I’m speaking to to research that person that I’m speaking to a little bit. I won’t spend hours, but there has to be some investment there.

So I’m against boring stock questions and I’m all for good, quality questions that have been thought about before, and the question that you asked is a good question. I just don’t agree with questions like, “What are your biggest strengths and what are your biggest weaknesses?”

Kirill: Okay, fair enough. And what would you recommend to people, how do you prepare for a question like that, “What is a conflict situation that you’ve had and how do you resolve it?” Do you put yourself in the good light, that you were the victim, or do you own it and say that you created the conflict? What’s the best way to approach this?

Richard: I think the key thing – and we touched upon it earlier – is be genuine and be honest. If you have an issue in the workplace and it was down to you, then be upfront about it and say, “I’ve learned from that.” You know, there is an argument to say you shouldn’t be completely open about that particular conflict because it might reflect badly on you. But if you can clearly demonstrate that you’ve learned from it, I don’t see what the issue is. So as long as you didn’t punch someone in the face — “How did you end the conflict?” “With one punch.” (Laughs)

You know, you don’t want to be bringing that up, but if it’s a situation where it was something that you disagreed with — I mean, in a lot of data science teams, one of the great things, and I imagine you can answer this far better than I can, is to have that environment of healthy discussion and perhaps disagreement, because out of that comes really good work. As long as it’s not aggressive, as long as people are approaching disagreements in a constructive manner, I can’t see what the problem is.

And as we’re referring to highly educated, very rational people in the majority of cases, the kind of conflict that they might have had, as long as they can clearly demonstrate — I’ll give you an example, the specifics. If somebody has left a job after three or six months, the person reading the CV is going to be questioning why that person has left their job so soon. And what a lot of people do is they try and disguise it. They say, “Oh, it was a great company.” You see this on LinkedIn quite a lot. “After 8 years, I’ve left a wonderful company.” You know, if it was that wonderful, why are you leaving? (Laughs) Sorry, that’s my cynical British attitude.

But what I mean is, on a CV, if you’ve only worked for somewhere for 3-6 months, the interviewer is going to think there was a problem there. It’s much better to say, “I joined the company on this premise. I had a meeting with my manager after 3 months and I explained to them that I wasn’t happy with the way things are going, “How can we improve it, how can I improve it?” It didn’t improve so me and my manager agreed that it was probably for the best that I look for alternative opportunities.”

That is a constructive discussion because you’ve sat down with a manager, it ended amicably. You know, as long as it ended amicably, I don’t see what the problem is with being open and honest and genuine. If it’s a major gross misconduct situation, then you obviously need to sit down and think about it carefully as to how you approach it. But at the end of the day you might as well be upfront about it and just explain what went on.

Kirill: Yeah, exactly. We are all good people at the bottom of our hearts. Sometimes things go wrong, sometimes things happen, but we learn from that and we move on. We ultimately want happiness for everybody and success and that’s what you should always remember.

Richard: Yeah. And if an employer is looking for someone perfect, good luck with that. As you said, most people are good people. Occasionally you go into a job and it doesn’t work out. It’s just the way it goes. Otherwise it’d all be like the 1950s where we’re in a job for life and we stay there 40 years and we get a gold watch and retire. It’s not like that anymore. People do move around and I’m seeing a lot of people move on after 3 to 5 years, especially in consulting they have real difficulty keeping hold of people after 3 to 5 years.

So, the key thing is just to have an open, healthy conversation. And when you’re interviewing for a job, you’ve got to go into that interview on the basis that you’re interviewing the interviewer as much as the other way round. And have that confidence in yourself. You know, a lot of your listeners have invested a great deal of time and energy in their training and development and education and work, so they should go in with a healthy amount of confidence – not arrogance, but healthy confidence in their ability and what they can bring to an organization. They should not shy away from difficult questions in an interview because the interviewer won’t shy away from it.

Kirill: Yeah, I totally agree. Thank you for sharing that. There’s just one more interesting thing I’d like to get your opinion on before we start wrapping up. You’ve obviously placed a lot of candidates into data science positions and various roles in different industries and companies. From what you’ve seen and from your perspective, where do you think the field of data science is going and what should our listeners look into to prepare for the future?

Richard: Well, I think that the main shift I’m seeing is more of an emphasis on machine learning, much more. I think that we are moving away from generalists. I think what’s happening is, when that famous article in Harvard Business Review from Tom Davenport with his colleague — I can’t remember what he’s called…

Kirill: Yeah, with the Chief Data Scientist of the U.S.?

Richard: That’s it. What’s his name?

Kirill: DJ Patil.

Richard: That’s it.

Kirill: Yeah, yeah. “Data Science is the Sexiest Profession of the 21st Century,” that’s the one we’re talking about.

Richard: Exactly. So, when that article came out, the term ‘data scientist’ was very new and it applied at that point to a very small section of people. Now the problem is that people’s definition of a data scientist is very different depending on one company to another. So, in answer to your question, I think what’s going to happen now is that there will be clearer definitions of data scientists, because one of the confusing things is that a data scientist in one company might be a consumer insight person, focused on consumer or shopper insight. And then at another company, it could be someone dealing with complex machine learning algorithms.

I think that really when you’re going in for a data science job, it’s really important to find out what kind of data scientist they’re looking for. So, I think for the benefit of your listeners—I mean, most of your listeners, are they recent graduates, or are they experienced people or is it a real mix?

Kirill: It’s actually a real mix. We’ve got people who are graduates, people who are looking for jobs, people who have jobs and are looking to change and grow, and people who are actually happily employed and just want to improve their skills. Even 10% of our listeners are owners, executives, directors of companies, who are actually hiring people, hiring data scientists. So we’ve got a big mix.

Richard: So, the first thing I would say is, define what type of data scientist you want to be. What do you want to work on over the course of the next 3 to 5 years? What sort of people do you want to be around? Your original question, “Where do you see it going?” I think it’s changing so fast—the problem that I have with the term ‘data science’ and ‘data scientist’ is that it’s so wide, and I think that’s where things need to be improved, is what is defined as data science, because I think at the moment it’s all a bit of a mess, if I’m totally honest.

Kirill: Yeah. I actually think, even though it’s a bit of a mess, like all over the place, I think it’s actually a good thing that it’s not stabilized yet. That’s where all the opportunities are. In accounting it’s all rigid, stable, there’s different CPA levels, there’s CFAs and so on, and it’s very straightforward what you’re going to be doing, where are you going to be doing it, what you need to learn. In data science on the other hand, because it’s not stabilized yet, that’s where you can really build a super career for yourself if you approach it the right way.

Richard: Yeah. And I think there’s going to only be more opportunities. There are some really interesting companies coming out of the start-up scene who are going to be hitting maturity at some point. There are also a lot of these start-up companies that are going through a period now where they’re in their adolescence. These companies are going to be major players over the next 10 years or so. I think what’s interesting, what you said about data science and where that will go, I think how work is going to change is really fascinating.

I deal with a lot of companies in Scandinavia. It’s really, really healthy, the way they approach work, because they’re always looking at efficiency. Whereas in the past, it was how long you sat at your desk. Even if you were playing Solitaire at 7:00 at night, it didn’t matter. Now I think we’re entering a real phase about efficiency and flexible working.

And I also think one of the things that we really need to work on in data science recruitment is embracing people who are over the age of 40, 45, 50, and getting those people into data science and giving those people an opportunity because I don’t think that there’s enough of that. I think that there’s an obsession, and perhaps an unhealthy obsession with youth, especially in Silicon Valley. And I think we’re missing, although there’s a big data scientist shortage, I think we’re really missing an opportunity of bringing people in from other industries, and also people that have got a lot to give and want to actually switch direction. They just need to be given that opportunity, and I think that’s where we need to improve, within recruitment but also within companies.

Kirill: Okay. Actually, that’s a very important point. Even though we’re short on time, I wanted to ask you to touch on that a bit more. We will make this the last question: What does a person need to do if they have experience in an industry that is somewhat adjacent to data science? It might be like databases, or might be econometrics, or might be programming, or some IT-related industry, or others like mathematics or physics. What do they need to do? How do they portray their previous experience?

Because a lot of employers are looking for data scientists with 5, 10 years of experience. People have those 5-10 years of experience, but not as a data scientist job description; they have 5-10 years of experience in an adjacent area. How do they need to portray themselves and sell themselves to still get those jobs that require that experience?

Richard: I think the key thing is it depends on who you’re working with. Once again, going back to one of the original points that we made, where when you’re applying for a job it’s much easier to explain — for example, if you’ve been working in software development, it’s much easier to explain how you can technically move across to a technical manager, rather than someone like myself, or even a human resources professional. Because they just technically might not be in a position to be able to get their head around it.

So what I would do, if I was in an adjacent industry, I would look at other people that have already made that jump. Once again, going back to LinkedIn, find people that have had a similar background to you that have moved across and ask them how they did it, because there will be clues there. And I also think, if you’re speaking to someone — I try my very best to be as open-minded as I possibly can, but if you’re talking to someone like myself, or another recruiter or human resources professional, articulate what your strengths are and the benefit that you could bring and just be upfront, you know, say, “I haven’t got exactly what you said on the job description, but this is where I feel strong and this is how I’m investing in myself.” And if you’re dealing with somebody that is prepared to give you a chance, they’ll give you a shot and relay that information to the technical hiring manager or the human resources manager if they believe in you.

It’s easy to be harsh on recruiters and say, “Oh, they’re all useless.” I know that a lot of people are not necessarily enamoured with recruiters, but if you have a good relationship with someone and you build a good relationship, the recruiter will go out of their way to help you if they believe in you and if they want to help you – and I’ve done that on numerous occasions, I’ve helped people find jobs that perhaps they haven’t been a perfect fit for that job, but I’ve believed in that person enough. They’ve probably sold me as to who they are and what value they would bring.

Going back onto the other thing, technically it’s important, but another thing that’s really important is what they’re like as a person. Are they going to fit into the team? Are they going to be easy-going? Are they going to be constructive in the way that they approach things? Are they hungry to learn? All of those things count just as much as the technical side of things.

Kirill: Gotcha. Okay, thank you so much. We’re going to end on that note. There are just so many questions I’d like to ask you, but we’re just short on time. Before we finish off, I’d like to ask you one last question — actually, let’s start with this: How can people contact you or get in touch with you? Because there’s probably lots of people who maybe have more questions about recruiting, or might even be seeking for help in finding a recruiter or getting positions. What’s the best way to get in touch with you and learn more about what you do?

Richard: Easiest thing is via LinkedIn. My LinkedIn profile is my name, Richard Downes, but you can find me under — I think it’s Richard A Downes, my profile.

Kirill: Yeah, we’ll include the link in the show notes for sure.

Richard: Yes, it’s linkedin.com/in/richardadownes. So that’s the easiest way. I’m going to be doing a series of really short video posts, probably very low quality because they will be done on my iPhone, but it will basically be just some hints and tips as to how to improve things from a CV perspective, or anything that really pops into my mind that I feel would help. I try and answer as many people as I possibly can, but obviously it might take a little bit of time to get back to people.

But if they do have a question for me, they can drop me a line on LinkedIn. If they want to connect with me on LinkedIn I’m happy to connect if they send me an introduction on LinkedIn as to who they are and what they do. I don’t connect with everybody because otherwise it just ends up a mess.

Kirill: And you mentioned you have a blog as well?

Richard: I do, it’s called ‘The Analytics and Data Science Pulse Blog.’ I’m waiting to produce another one. It’s been a little while since I last did it, but you can look on my posts and activity. And I’ve done Q&As, there’s over 30 on my profile, all about data science and hiring data scientists, how to become a Chief Analytics Officer, how analytics and data science have changed retail. If you look at my posts, there’s lots and lots on there.

Kirill: Okay, we’ll include a link to that as well. Thank you for sharing that. And one last question I have for you: What’s a book that you can recommend to our listeners to help them on their journey into data science?

Richard: God forbid, Kirill, because your listeners would be far more educated than me, but one of the things I read which is a gentleman I connected with ages ago was “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die” by Eric Siegel, who is the founder of the Predictive Analytics World conferences. I found that really useful when I was starting out, but obviously, as I said, you’re talking to a recruiter, so from a technical perspective it’s probably not the very best book, but it broke things down on a basic level for someone like myself.

Kirill: That’s really cool. And we’ve had other guests recommend this one as well, so “Predictive Analytics” by Eric Siegel, check it out if you want some intro into analytics and what it’s all about. Once again, thank you so much, Richard, for coming on the show. It was such a pleasure to host you and I’m sure a lot of people are going to get so much value out of this. And you don’t know, maybe you’ve changed some people’s lives by the tips that you’ve given and people will get jobs from this.

Richard: I hope so. I really believe in helping people. As much as my job is a difficult one, if I can help people improve and get a better chance at their next job, and that they’re successful and happy, most importantly, then it’s all good. And I appreciate you giving me the opportunity to have a chat.

Kirill: My pleasure. Thank you very much, Richard.

Richard: Thanks, Kirill.

Kirill: So there you have it. That was Richard Downes from Stirling Global, data science and analytics recruiter and headhunter. I hope you enjoyed this podcast. Personally, I learned quite a few interesting new things from here. My favourite thing from what we discussed with Richard was the way the industry is shaping up. At the moment, it’s not structured, it’s not stable yet, and therefore there are lots of opportunities for careers.

And it’s also interesting to hear how Richard mentioned that you need to understand what you think a data scientist is, what you understand as a data scientist and then find companies who imply the same meaning in the term ‘data scientist’ because there are so many different things that a data scientist can do.

And if you have any additional questions or things that you’d like to discuss with Richard, then you can always contact him on LinkedIn. And especially if you are looking for a data science job, I highly encourage you to reach out. It’s always good to have a recruiter on your side. Of course, you can continue to search for jobs on your own and apply directly, but often a recruiter has much more visibility and can provide you some additional tips and advice, so it’s always a good idea to at least explore this option.

And there you go, Richard actually went through the trouble of coming on the podcast, so he would be a good person to get in touch with and have among your connections. You can find Richard’s LinkedIn in the show notes at www.www.superdatascience.com/77. There you can also find links to his blog posts and any other materials that we mentioned as well as the transcript for this episode.

On that note, I hope you have a fantastic day and your searches for analytics jobs, if you’re searching for one right now, are going to be successful and that you’re going to be super happy in your career. And to finish off, I’ve got one small request. If you know anybody who is looking for a job in data science, forward them this podcast, it might help them find a more fulfilling, more happy job and they will thank you for it later.

So, take care of those people around you and if there’s anybody you know, make sure that they listen to this episode. And I look forward to seeing you here next time. Until then, happy analyzing.