SDS 101: What a Data Science Headhunter is Looking For - SuperDataScience - Big Data | Analytics Careers | Mentors | Success

SDS 101: What a Data Science Headhunter is Looking For

Welcome to episode #101 of the Super Data Science Podcast. Here we go!

Today's guest is Director of Talent Acquisition at Collective[i], Urie Suhr

Subscribe on iTunes, Stitcher Radio or TuneIn

Finding job opportunities and preparing for an interview in the field of data science may feel like an uphill journey. In today’s show, we’ll share insights that will be valuable in helping you get started.

We’ll share tips on how you can better expose yourself to opportunities in the first place, and how to position yourself as the best candidate when an opportunity arises. Even if you’re not looking for an opportunity right now, today’s show will be surely be useful as you never know what will come in the future, so it’s always good to be one step ahead of the game.

Let's get started!

In this episode you will learn:

  • The Challenge of Finding the Right People (06:37)
  • Explaining Why You're the Best Fit (08:45)
  • Networking Is Powerful, and Easy (15:04)
  • Make Yourself Visible to Recruiters (23:13)
  • Focus on Where You Want to Be and Tailor Your Message (32:40)
  • Years of Job Experience Rarely Match the Job Description (35:54)
  • Connection to the Opportunity Is More Important (40:54)
  • Leverage on Your Background (45:14)
  • The Future of Jobs in Data Science (55:50)

Items mentioned in this podcast:

Follow Urie

Episode Transcript


Full Podcast Transcript

Expand to view full transcript

Kirill: This is episode number 101 with Director of Talent Acquisition at Collective[i], Urie Suhr.

Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, Data Science coach and lifestyle entrepreneur. 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.

[Background music plays]

Hello everybody, and welcome to Episode 101. Congratulations on starting this new second “hundred” of episodes on the SDS show, I’m super excited about this.

I decided to start off this new hundred with a very dynamic and energetic guest. Today we’ve got Urie Suhr on the show and Urie is, in short, a headhunter. She is the Director of Talent Acquisition for Collective[i] which is a data science company which uses artificial intelligence and predictive analytics to help their clients with things like sales, CRM and creating a better customer journey.

What we talked about today with Urie was the way she goes about finding candidates and recruiting people and headhunting talent. So, I think this is going to be a very useful, very powerful episode, to see the perspective of the other side. To see the perspective of a headhunter, how they think, how they look for the best people in the field, because that will allow you to better position yourself as the expert, as the person that’s most passionate about what they do and it will hopefully help you expose yourself to these opportunities better. And even if you’re not looking for an opportunity right now, I think these tips are going to be useful anyway because you never know what will come in the future. You never know where the world will take you, so it’s always good to be on the front foot and one step ahead of the game. I can’t wait for you to check out all these tips and without further ado, I bring to you Urie Suhr, Director of Talent Acquisition at Collective[i].

[Background music plays]

Kirill: Welcome to the SuperDataScience podcast everybody. Today we’ve got a very exciting guest, Urie Suhr with us. Urie, welcome, how are you?

Urie: Thank you so much for having me on your show. I’m doing fantastic.

Kirill: That’s awesome. And where are you calling from?

Urie: New York, New York.

Kirill: New York, New York. How’s the weather in New York, New York right now?

Urie: Transparently crappy [laughter] it’s going through its seasons now but I’m sure it will level out.

Kirill: Are you looking forward to the winter?

Urie: Yes and no. I mean of course four seasons always gives us the range of, like, the pretty seasons changing and all the different type of activities and so forth but, you know, you got to do what you got to do. I think summer is actually probably the favorite.

Kirill: Favorite, yeah. I’ve been to New York but only during summer and it was surprisingly hot in New York. I think it’s because of so much concrete, you just feel the heat very strongly.

Urie: Yes, and it traps all the humidity. So, it’s fun times here. [laughter]

Kirill: All right. Well, thanks a lot for getting in touch. The way we met was on LinkedIn and I really like LinkedIn. It’s phenomenal because you can get to meet so many interesting people, and your background is definitely fascinating. So, tell us a bit about how we connected and what you do that made you look for content in this space.

Urie: Yeah, absolutely. How I started off definitely being fortunate to connect with you, is that I was actually sourcing for an instructor that could teach more on the AI Deep Learning side of the house in the world of data. And you had come up of course through all the course work programs and stuff like that that you had visible, not just only on LinkedIn but just in the online space, so I was really excited to do that and of course that’s how I found you.

What I currently do is I’m the Director of Talent in Collective [i], it’s an AI company in the B2B space and what we do is we capture data around B2B sales activities and so we use our AI and predictive analytics by merging clients’ CRM and other relevant sources of data into our network that converts raw inputs into the intelligence to enable enterprises to be customer centric and data driven. So definitely, you know, it’s a great space to be in especially surrounding a lot of ambiguity when it comes to forecasting sales predictions and so forth, and everything in between in the B2B sales space.

Kirill: Fantastic. So, it’s kind of like an analytics/consulting company but oriented into the space of AI and leveraging the power of AI as I gather, is that right?

Urie: Definitely, and more so in the specs of, there’s two aspects of our business of course, our application and building out on network. A lot of the times, especially trying to make that connection between understanding the behavior or patterns of certain types of relationships when it comes to of course business to business sales, and you always want to know what your data and that what you’re capturing too as well can really give you a more accurate ability to connect with the right people in that space. It does, of course the application as an individual, we have access to your data, we monitor it and everything like that and so forth, but also, we’re calculating our measurements through, not just our predictive modelling, of course coming from the data science team, but when it comes to more on the AI side of things, it’s the neural network that we have, and also just building on the deep learning side of things to really go deeper into finding solutions such as that.

Kirill: Fantastic. And so, you’re in charge of getting people on board, of getting those AI professionals on board and recruiting them so that they can help with this mission. How’s that experience for you?

Urie: Fun but challenging. [laughter]

Kirill: Yeah? Is it hard to find or is it hard to find the right people?

Urie: I think it’s a little bit of both because you know I always, I’ve been in this space for quite a while now, over six years, and what I found is I think the disconnection when recruiting for certain types of talent especially in this field, it’s also the market’s responsibility to understand when new technologies emerge. Yes, there’s going to be opportunities from company to company related to whatever project they’re working on, that the team has the ability to ramp up on those skills. Right? At the same time, sometimes maybe that isn’t exactly the time or place that the company can do that or have that particular talent in that space, have that bandwidth. So it’s important, you know, these programs such as yours and so forth that really can give the opportunity for those who are looking to, I guess, refine their skill sets or so forth or be part of that marketable space, that becomes a lot more easier or achievable.

But as far as like recruitment, yes. And again, the difficulty lies in, it’s part of candidates not being able to clearly make that distinction of what they know versus what they’re doing. You know I think it’s very interesting, through speaking with a lot of candidates and so forth, they possess a great amount of knowledge in a certain field or a certain tool and so forth and they go and explain all these things that they know but they’re not really explaining how they’re applying it. And a lot of the times hiring managers, and I’ve discussed this with many hiring managers and working with many different companies in the past, that that is something that is a disconnect between the hiring process.

Kirill: Is that because they don’t do enough research into what they’re going to be doing or what’s the reason for that, do you think?

Urie: That’s a great question. It’s both. It is with researching the company’s background but also saying, okay so given a situation if there is a question that’s being posed by someone from the team during an interview process, when they ask what does your day to day environment look like and what tools are you using and what are you solving for? Candidates always rise to that answer by explaining, okay this is kind of my day to day activities, but they’re not really explaining why or how they’re solving for what they’re solving for. Of course, when you’re looking for new opportunities, whether you’re in the beginning stages of your data science career or in mid stages or whatever stages you’re in, it’s really important to clearly express that, yes, for example, the reason why I chose decision trees is because of X Y and Z and here is why and this is what was relevant to the work that I was doing. Because it really does help hiring managers understand where your perspective is coming from, why you’re thinking the way you’re thinking, and how you’re applying that and how you’re solving.

Kirill: Gotcha. Thanks for that insight and we’ll definitely get back to the interviews in a moment. But let’s go back a bit and talk about your background. You haven’t always been at Collective[i], you’ve gone through a very interesting career path. Tell us about your own journey. Like where did you start? You said you’ve been in this space for six years, but prior to that were you in data science already or were you doing something else?

Urie: Actually, I wasn’t. My background comes from media and advertising with a minor in sociology, so completely wildly different from what I’m doing now. I would say I’ve always been data driven, it’s always been something that has been a deep passion of mine for, since I was very young, and I was definitely one of those obnoxious kids that always asked the whys on everything that was said by a teacher or so forth. In regard to why I got into this space, I’ve always, again, had a passion for a deep analysis in any direction. Meaning any manner of topic, right? And it’s really important for me to really understand how data science and analytics can drive our ability to explore questions and solve problems by learning from the results that were derived using data science principles and *mythologies [00:10:45.3]. Right? Exacting results and calculated measurements from unstructured data really does allow us to achieve higher accuracy and it helps us at the end make better decisions and observations as well. I’d like that clarity that it can provide, granted nothing is going to be an absolute because there’s always something to learn from how we decide to solve a certain problem but that is the reason why I got into this space. It’s just been a really fascinating field and also, it’s going to really make a great impact in not just like the years to come, but decades to come.

Kirill: Yeah, yeah. Definitely agree. And so how did you first get into the data science space?

Urie: Sure. I actually worked, prior to Collective[i], worked at General Assembly, otherwise known as GA, and there I recruited for engineers on the production side and also recruited globally for all of the technical instructors. I built out two programs, I built out the android, the ADI otherwise known as the ADI team over at GA and also the data science team at GA as well. Through that I was really fortunate enough to not only be surrounded by those who empower you to do what you’ve always really wanted to do, but maybe didn’t have the– I wouldn’t say confidence, but maybe it wasn’t something that was in your radar or you thought you had the ability to do it, but I really did get to explore that opportunity when I was working at GA and as a result having the encouragement from all the different types of teams and all the different types of disciplines that were at GA, I decided that the wheelhouse that I was interested in most was data science and it just felt it was the best decision for me.

Kirill: Yeah, okay, Gotcha. And then you decided just to say in this space and develop this career further, where you’re helping build out teams. Did you, do you yourself look into data science like I think you told me that you did a course on data science or something like that?

Urie: I did. I ended up taking the evening course at GA in data science and I will say this. Since taking that course and I took it earlier this year, it’s been a complete transformative year for me in terms of my career.

Kirill: That’s fantastic.

Urie: Yeah. It was just, it’s a wild journey so not only did it accelerate my growth in the data science world, but in terms of recruitment and finding talent and connecting with people, I felt even more knowledgeable and relatable in the space. It’s definitely helped me with not just that but it’s just a different world. It really completely transformed me because I realize now- In the past, I recruited for data scientists before so you know it wasn’t any new news in the recruitment aspect of things, but now I realize connecting with candidates in the current time being, it really not only helps to understand what the candidate is saying but it does create a sense of, I don’t know, it’s a deeper understanding. That connection is fantastic because I do, as far as the conversation and flowing and also not only on the candidate’s side but being able to really connect with my data science team at Collective[i] as well, too.

Kirill: I can totally understand that and, yeah, that’s a really cool step that you took in taking that course. Do you think you’ll do more courses in data science?

Urie: Thank you. The short answer is yes, I hope I can find the time [laughter] and it’s definitely busy wearing the director hat but absolutely. I think my focus would definitely be going more towards on refining my skills like in NLP or especially what the team is doing in AI and deep learning.

Kirill: Okay, Gotcha. Thanks a lot for that Urie, that’s a very interesting background but now let’s get back to the data science side of things. There’s so much that I want to ask you, I always love when recruiters come on the show. I really don’t know where to start myself. But let’s start with maybe some general tips and advice that you can give to our listeners because there are lots of listeners who are either actively looking for new opportunities in the space of data science or they’re just open to them. To those people, what could you say?

Urie: Absolutely, great question. One thing that I definitely would like everyone who is listening to the podcast to take away from is that how valuable networking really is, and networking means that it can stem from anywhere. Now I recognize and understand a lot of people have said, well you know I go to LinkedIn or I have a Twitter and so forth, but it’s also the activity and just being very engaged in the sense of whether even if it’s passive or actively looking for opportunities, it’s always good to connect with your community. Whether that be going to that meet up or making that connection or going to all these events that may not be even relatable to particularly the job that you’re looking, for but if it is in the wheelhouse of data science, you never know who you meet.

For me, I mean I can say on the flip side that’s how I met amazing candidates. It’s through searching through not just looking at your LinkedIn and being a super stalker on all these platforms that recruiters are empowered with to go look for candidates, but it’s just really making that connection with someone, admiring them for what they put on Twitter or just really thrusting yourself in this community and saying, hey, at any given time I can connect with any one. Another source that I found really interesting is Kaggle is definitely obviously a great place where you can work on your own skills and so forth, but it is also a community that you can connect with others as well. Or maybe there’s someone that you spoke to at Stack Overflow because you came into a certain challenge that you couldn’t overcome regarding something in code. There’s many different ways to network but at any given time never minimize yourself by saying, okay well this is where it’s going to come from and that’s it. Right? It’s just always being able to network at any given time.

Kirill: Gotcha. And that brings up an interesting question. Let’s say I’m on Stack Overflow, I chatted to someone because I had a question and they helped me out and I asked them another question, they helped me out and then I said thank you and so on. And that’s an initial start to networking, it’s like the spark between two people who meet online, but where do you take it from there? Like as a person who ultimately wants to network, what do I do next? How do I chat to that person more? Do I invite them to a coffee or do I catch up with them on Skype? What’s a next step from there so that that initial spark and initial connection doesn’t just stay an initial connection and then die off from there.

Urie: Yes. My immediate thing would be definitely to reach out and ask for you know, hey, I’d like to ask if you have this space or the bandwidth, to ask deeper questions, very interested in regard to learning more about you and so forth. Definitely ask for the email also ask if they have a LinkedIn or a Twitter that they can follow. Right? These are things that really keep the opportunity open to as well especially in timing. Right? If you’re not, again, if you’re a passive candidate and you’re like, hey you know eventually I’d like to work at XYZ company, this is a really great way to open up that door and open up that opportunity. Again, the question is not networking but if this person is teaching you something that is valuable to you at the end, the connection serves its purpose regardless, right? Most importantly, rather than how you’re connecting to someone and asking for these things, A, diminish the thought of being too timid. You’ve got one life, you’ve got one shot, go for it. It’s okay. Be direct be like, hey, you know XYZ, really love what you do, want to learn more, is there an email that I can speak further upon or search and so forth LinkedIn, ask for the Twitter, but always also keep remembering, point B, is that to make sure you do it within the 24-hour timeframe. I know a lot of the times some people sit with the email and go, humna-humna-humna, I got the, yes, and I wonder when do I reach out? Okay I’ll give them just a breather because I don’t want to be in their space. But truth of the matter is the connection, you always want to reach someone at their peak of that conversation so make sure that the turnaround time is very tight and it’s within 24 hours.

Kirill: That’s a very good tip. I totally, totally, I can agree with that. They’re never going to like you more and like the more time passes, the less they like you in a sense that the less they remember you and the less they remember the interaction with you, and so yeah you got to get on that train early.

Urie: Absolutely. Even in the perspective of maybe the initial interest decreases, but I almost want to say the initial interest decreases because of the fact that if you don’t know that person well and you just connected with somebody, everybody can say that they always have at every given time point they have something to do. Right? Your brain is like in 50 million different places. Then all of a sudden three hours later, you know sometimes I can’t even remember what I ate for lunch, so to make that connection immediately is very important. That’s very valuable.

Kirill: Totally. Totally. I wonder to your point about solidifying the connection in at the start. I want to give an example that I had in my own experience and so when I once read an article online like I don’t *three-eight [00:20:28] years ago I think I was still at Deloitte maybe, four years ago, I really liked it. It’s the data scientist type e-article by Ben Taylor, it’s very popular among data scientists, a lot of listeners would have read it because I already spoke about it, and I really liked it so much that I actually wrote Ben Taylor a note saying that, hey you know I really liked your article, very, very excited, thanks a lot for sharing, this is really cool. And then he replied and then maybe I messaged him again and that’s it, and kind of left it there. But that was like the initial connection and initial also action that I made him aware of me and I told him that, hey this is this.

Then later on like a few, maybe like a year later, he actually messaged me because he was coming to Australia to do a talk in Sydney and he said, you know if you want to meet up, let’s meet up or can you also give me some tips on the Australian audience? I was like, wow this is unreal, he’s messaging me. Unfortunately, I couldn’t go to Sydney, because I was in Brisbane but I gave like a huge list of things to look out for, like the tall poppy syndrome in Australia and other things, and he was very happy with that because I’d helped him with his presentation, like restructured it, tailored it to the Australian audience. Then again, many years passed like one or two years passed and then once I had this podcast I reached out to him again and I said, hey do you want to come on the podcast? And he agreed. This is a person that like three or four years ago I was really looking up to and he was like writing articles and it was very inspiring to see that he took that up and that he was happy to connect and get in touch and none of that would have happened unless I had messaged him the first time right after I read his article, when I was like so inspired by it. So yeah, that’s one example of that.

Urie: Absolutely. And you know hearing your story, that’s fantastic to share it to say follow your passions first, you know, what are you really obsessed with in this field? That’s what always leads to a stronger connection, right? It’s not just about trying to get your foot in the door of an opportunity but it’s what are you really passionate in? What is something that, you know, have you been trying to solve a certain problem in this space where you’re identifying with somebody else? So I think that’s fantastic.

Kirill: Yeah. I totally agree. Now let’s look at your side of the equation. Let’s say, actually can you walk us through the process when you’re looking for somebody, for a candidate. You need to fill a position like somebody in deep learning or a data scientist or a visualization expert, you have the job description, the criteria, so how do you go about this headhunting process as it’s called? What are your first steps, where do you look? Do you look on LinkedIn, do you look on Twitter, then do you look on blogs? How do you go about finding that person, which media do you use?

Urie: Wow. This is the secret ingredient that I’m going to –

Kirill: [laughter] Well, whatever you can share, whatever you can share.

Urie: Absolutely. Happy to share. I look everywhere, and again, just like for a candidate to look for job opportunities, I do the same in finding very various types of talent. And what I mean by various types of talent, I mean we speak about diversification but I’m also speaking about diversification of whether your background comes from more of a scientific background or a CS degree or math, it can come from anywhere.

Where I like to look? Yes, LinkedIn is a great source because of the amount of people that it has on that platform. However, I always do like to cross reference all of the candidates or try to see where there’s other opportunities to look for. So yes, I am that person that stalks you on Kaggle, I’m always curious about that. Twitter actually has been a great form of connecting with candidates as well because I’m more of a, hey I really want to learn more about this person, and then if I like what I see then I’d really like to reach out to that person and go, hey, do you want to learn more about us?

It’s really about following those who are really, really, truly passionate about being in this space. Not for the reasons of, again, by the ends of means or so forth but just really, really, love what they do. That doesn’t only just live in LinkedIn, it does live on Twitter, it lives on Kaggle, it lives on Stack Overflow, it lives in Quora even, or even sometimes Facebook for work. Even community groups as well. There are a lot of groups that I am a part of and through there just having that organic connection and having that conversation, again, bringing it back to networking. It’s so important because I feel that networking provides the more organic approach of connecting and learning about each other, so both people are comfortable and then really speaking about the opportunity.

Kirill: Yeah, I’ve gotcha. You look for a person, you find them on some media, like you find an interesting candidate and then you actually cross reference. I found that point interesting, you cross reference or cross check or like explore this person more on other platforms. They might write a lot about, like I might have written a lot about myself on LinkedIn but then where’s the substantiation of that, right? You’re going to go and look on my blog, you’re going to go and look on Udemy, you’re going to look in other places to make sure, or not just make sure, to learn more about what is it I’m doing, that I’m not just like sayings things that I’m doing, printing up the picture for recruiters but you’re actually looking at the insights of the actual value that I’m bringing to the world and what exactly it is that I’m doing. It is also a good way to check that the person is indeed passionate. If they’re passionate on data science then they’re going to have a blog or they’re going to write stuff on Twitter or they’re going to have I don’t know like a Tableau Public profile or something like that, so yeah, that’s a very interesting point, the whole cross referencing. So, what is your recommendation then to candidates in this sense. Just have as many different profiles as possible, or have like several and go as deep as possible in each one?

Urie: I would definitely say, you know, make sure that your presence is more than just one place, so don’t just have a LinkedIn and be like, okay everybody knows about me. No, show the versatile nature that you have in the field of data science, right? Let’s say someone is a fanatic in really refining their skills in Python and so their Twitter feed is just all about, hey, I just want to make sure that I’m sharing with the community what I like and what I’m learning. These are kind of the feeds that show case, right? Then on LinkedIn it could be a more in-depth experience or in-depth profile but a general snapshot of all the things that you’ve done and your activities. Or let’s say that you’ve done a Kaggle competition and you’re in Kaggle and so forth. Those kinds of things when they’re all combined together, it really brings a whole picture of who you are.

Kirill: Yeah. Totally agree. Okay, cool. Thank you for that. Any other tips for people in the space of data science that are looking for opportunities?

Urie: Absolutely. You know, so-

Kirill: I love it. You’re just full of information. I was telling, by the way guys, I was telling Urie at the start of the podcast that it’s like 8:00 what time is it in New York right now?

Urie: It is 6:40 right now.

Kirill: 6:40 pm. But in Australia it’s like 8:00 am and I just woke up like an hour ago, still waking up. But it’s so cool to feel your energy. You have so much to share you’re like, absolutely! So yeah, let’s go for it.

Urie: Thank you. I think well, when I began my journey and especially just getting into recruitment in general, these are the same issues that I ran into. I’m like, well, how am I supposed to find the opportunity or oh my God, you know, do I reach out to that hiring manager or when is the right time, or am I bothering them, or all these questions combined. Right? So, I completely understand when someone especially in the start of their journey in a new field or new space, there’s going to be all these different million-dollar questions anybody wants to ask for someone who is on the HR team. Right? Like do you actually read my resume when I upload it onto your company website? Things like that.

I would definitely say that make sure when you are connecting for a certain opportunity and applying and so forth. I will use LinkedIn as an example. There have been many times where candidates whether again they’re junior candidates or even mid-level and senior candidates. They will reach out and be excited and express that they are looking for an opportunity within the company that I’m working at. Right? But 99% of the time candidates fail to say, they forget to express why and the reasons why they want to work for the company that I’m working at and also more importantly the link of the job description that they applied for.

And I get it guys, anybody who’s listening who’s a candidate who’s gone through this. It’s fantastic, it’s okay you should definitely reach out. Right? Better safe than sorry, so just go for it. But first of all, make sure that your message is curt. Make sure that it’s to the point, make sure that you’re speaking about the company first, what attracts you to that company, and then how your skill set can be applicable to that role and then also please kindly leave the job description. Because guess what, guys? When I’m looking over thousands and thousands of resumes and someone says, Hey I’m so excited to apply for this company, please let me know if there’s room for me here, and I have the opportunity to connect with you on the opportunity, I’m like, which opportunity? Because there’s lots of them. And at any given time, I just don’t have the bandwidth or time to match you to an opportunity.

Just make sure, the easier you make it for anyone on the internal team, the HR team and so forth, the faster they can navigate that process for you. It really isn’t, and I hope that I can speak to all of us in the HR world. It’s not that we don’t want to help anyone or that we don’t appreciate someone reaching out like that or what we think about that. It’s just the amount, the bandwidth that’s given to us on a daily basis to balance out so many different hats that we wear. That makes it very direct and easy for us to either, A, push you through the process a lot faster or even delegate that to the recruiter who is working on that opportunity.

Kirill: Gotcha. That’s very interesting. But do you think that some of that is because sometimes people reach out to like 10 different recruiters at the same time and they don’t even know what job descriptions they are applying for, they just know that there’s this company, this is an interesting company that they do what I like. I’m going to contact this recruiter, I’m going to contact this recruiter, and they maybe even copy paste the same message across the board, just because they’re doing it very quickly, they’re like looking, and they know that the probability of them getting a position is very low it’s like maybe 1% because there are so many other candidates, because recruiters don’t have time, lots of factors will go into that and they know the probability is low so that they try to play the game of numbers. I’m not saying everybody does this but possibly there are people who do this and they just send out as many connections that they can and whoever replies to them, then they will look in further and understand what job description fits them. What would you say to people who are applying that type of strategy?

Urie: Wow, that’s a really great question. So yes, agreed that by numbers, it’s something that the individual can’t help but think about. When you play it by numbers is it because- what are your circumstances that you are just looking for any opportunity and seeing what connects, right? Is it the start of beginning a journey in data science? So that’s understandable. But at the same time, you have to start thinking about, okay, the quality of connections is going to be important, it’s going to make a larger impact in future terms. Not just future terms meaning the growth of where you want to go in that opportunity, right? So, is it worth 20 different opportunities that you’re just standardizing and then just connecting with one or two in something that you know like, okay I’m just going to connect to this, or really going for the place that you really want to work at? I would probably suggest, yes, I understand when it comes to also time sensitivity or any given time stance where someone’s looking for a new opportunity right away, to reach that type of volume a standardized approach might look like it’s ideal. But I would highly say that if it is a company, I guess I could say if it’s a company that you are really, really, really, wanting to work at, that tailored message is going to go a long way including that job description.

Kirill: Yeah. I totally agree. And I got a story here from my friend who’s wedding I was at just like a week ago. People on the podcast might know Vitaly Dolgov, he is my mentor he was on the podcast as well and in a special episode. His wife now, she really likes this one hotel and she is in the industry for, what’s it called like when you’re working at hotels? And so basically, she really wanted to work for this one hotel but she didn’t know how to approach it in the sense that okay, like I’m going to apply but what do I do? And he said, look, just write a very, very, tailored letter why you really, really, want to work there and then she wrote that and then when she went to the interview, her friend actually told her. Her friend went to the interview like a day before her, and then they ran out, the HR manager ran out saying, hey, are you Amy, are you Amy? She’s like no. And they’re like, oh, that’s okay.

Then the next day she comes over and she’s like she’s Amy and so she’s like, are you Amy? And she’s like, yes, I’m Amy. And they’re like, you have the job. You have the job, just tell us, we’re just going to have this chat just so that we know that you’re happy with the position and everything. We totally loved your letter, this is the most incredible letter we ever got, you definitely have the job. You’re starting like in the next week. And she was like, wow, this is so cool.

Urie: That’s incredible.

Kirill: That’s how far a letter can go.

Urie: Absolutely.

Kirill: Okay. So that’s a good tip. I had another question for you. Let’s talk about the chicken and the egg problem. In data science, there is this chicken-egg problem everybody is probably a bit aware of it. That in data science, it’s like an exploding field and more and more companies are getting on data scientists on board, more and more people want to have data driven decisions, more companies want to be powered by data, and at the same time people are also getting into this space because they see the value they can bring as data scientists. But all these job descriptions, a lot of the time they say, we require four years of experience, so we require six years of experience. That’s great but you cannot get the years of experience unless, you know, you get a job. It’s kind of like that’s the whole, the chicken and the egg or the vicious cycle problem. How do you get the experience if you can’t get a job, because this field is so new? What would you say in that case? Like if a person is really passionate, wants to apply for a position but it clearly states on the job description that you need six years of experience in data science to apply for this position?

Urie: That is the million-dollar question, isn’t it? [laughter]

Kirill: Totally.

Urie: Insider’s secret, a lot of the times when recruiters, people on HR, they have to write those requirements, it’s almost an ideal. It’s saying, hey if this person exists, lovely unicorn or a purple squirrel out there, please apply, you know. Obviously, we’re going to keep the light on for that person or that individual who possesses the rainbow of skill sets, and then some. However, I would suggest this. If you feel that like for example that you have used, six plus years of experience is what we’re looking for, you have to know XYZ stack and so forth, I would say that maybe you’re within like a couple of years short, maybe you’re four plus years, I would still encourage that candidate to apply.

But it’s also applicable to what tool sets you have. I want to say that if your skill set only matched 10-15, 20% of that job requirement, more than likely it’s not going to work out. But I would say if you feel like you’re anywhere between 65-80%, go for it. Because you know again when writing out those jobs description, understand that it’s very interesting when you are trying to find an addition onto the team, it really depends on that technical environment. Maybe it’s a brand-new role, or maybe it’s even a role that needs to be filled because someone else moved out from that opportunity. Whatever those reasons may be, a lot of the times these job’s descriptions are written out based off of their perspective of what they’re working on currently so you can imagine that in every technical environment, they’re doing XYZ and doing all these things, so in them, they’ve been in that space, so that’s why these requirements look like a laundry list of things. But at the end of the day, I would say when the process of finding talent begins and when the interview process really begins for a lot of these companies, they soon discover that, hey, you know why don’t we go ahead and, I think we could be flexible with the criteria here, because now we’re recognizing that this is more important. Right?

So that’s why I always say just because you don’t match the job description perfectly, don’t be discouraged to apply. Again, be thoughtful of how you’re applying. Like I said if let’s say that you’re going for a data science role, clearly you need Python and you need to understand what different models there are and things like that and having that kind of experience, versus someone who’s a data analyst as he says, yes, I know SQL, I mean clearly that’s not going to make that connection. But, again, just really take a look at the job description and fill it out of what the job description really entails or what the work is going to be if there’s a good description of it, and then go from there and apply. Right? Because it’s about the substance of the role, not always necessarily saying that you have to have all of the skill sets for that role.

Kirill: Yeah, gotcha. Do you ever hire people for their potential to grow? So, you hire a person, you know that they don’t have the right skills but you see how passionate they are, how excited they are by this field and you’re like, we’ll hire them and we will invest into them and they will grow and they will become the perfect, as you say, unicorn once they’re on board and they’ve been with us for like a year or six months or so. Is that something that you’ve done before, is that something that’s common in the industry?

Urie: I’ve done in the past and currently do so now. [laughter] Because realistically especially how fast, as fast as technology moves, especially when you want to do a deeper dive and just talking about the technologies that are surrounding data science right now, I feel like the field of data science still isn’t that old. When it’s being really truly recognized what, it’s seven, eight years, maybe nine years max? Then when we talk about going into deep learning it’s what a couple of years old? So again, yes, that ability to solve problems is going to be more relevant or just as relevant than saying that I have all these skill sets.

I’ve seen and passed on perfect resumes because of the fact that it didn’t feel like that person wanted to grow with the opportunity or really had the connection of the opportunity. It felt like they were like, okay, I have this skill set and I know it’s a good skill set and that’s that. Right? I could perform these services and that’s where it goes. It really depends on the opportunity as well. Every company has different technical environments. For us it’s about, hey you’re talented and you seem to think about things in many different ways and how you’re solving problems, but also not afraid to make mistakes in order to achieve the right results. That’s actually very important in the field of data science that I find.

Kirill: Gotcha.

Urie: So, yeah.

Kirill: I totally agree. And I like that comment about the connection to the opportunity is more important than a perfect resume. That is totally true. As a business owner and at SuperDataScience, that’s exactly what we look for. Because we’re growing our team and we have people applying for different roles like where there’s developers, designers and so on, and then there’s always like an interview process and I have the final interview but I always look at not just the skills but how excited is the person by this role, by the company, by the company’s mission.

Because ultimately businesses want people on board not just who can do the job but people who will push the envelope, who are going to push this mission forward who are going to help the company deliver on its promise to its clients, to its customers, to its stakeholders and ultimately most companies are here to make the world a better place and the people need to be aligned with that, with the mission of the company, the approach that it’s taking. If you’re just like this really cool data scientist with all these skills in your resume but you’re completely disinterested in the company’s mission, well guess what, you’re not going to stick around for long, you’re going to be there for a year or two, you’re going to do a [inaudible 00:42:58] job or maybe you’ll do a great job but you’re not going to go above and beyond to add to that, I don’t know, life, love, energy into the company, into the mission itself. And that’s what everybody is looking for.

Urie: Absolutely. Because I feel that compassion is what ultimately measures or maps out your success. That’s how you ultimately become successful, because of that innate drive that you have and because you’re compassionate about that particular project or the field of search that you’re in. Absolutely, I agree.

Kirill: Yeah. And it’s good for the candidate as well, it’s good for the person, right? You’re going to be much happier if you connect, if you have that compassion, if you’re growing with the company, if you’re not just doing a 9-5 job for providing a service, but you’re actually growing personally and professionally. Like I always say, if you’re not growing at a company, if in your current role or in a role that you’re applying for you’re not growing or you feel you’re not going to be growing, you should either quit your job or you shouldn’t apply for that job. Because when you’re not growing, you’re dying. We’re here to grow on this planet. That’s the only thing that we can continuously do and if you’re not doing that, personally, it’s my opinion, but I think it’s a waste of time.

Urie: I absolutely agree. And I always say that those who are on the side of like, I’m perfect and I reach for perfection and so forth and, you know, I have very minimal mistakes, well guess what. If you say that you make minimal mistakes, then you clearly, I feel, don’t really understand the true measure of success. You have to fail to feel success.

Kirill: Exactly, exactly. And I had another question. I’m really loving this podcast. This is like super energizing.

Urie: Me too.

Kirill: I had another question for you. Do you look at past backgrounds of people? There are people listening to this podcast who are coming into data science from a completely different field. Like yourself, you came from media and sociology as you say, or somebody might be coming from an arts background, somebody might be coming from a literature background, from a commerce background, from different backgrounds and maybe they have a couple of years’ experience in data science now. But do you ask people about what they did in their past lives and how they’re leveraging those skills? Does it matter to you as a HR person or as a recruiter what other experiences and skills they have apart from the analytic side of things?

Urie: Absolutely. I will say this, I think that what you’re passionate in is going to again ultimately drive your success. Right? Yes, in this industry especially in the field of data science, it does come from more of an academic background. Someone who is deep in the field of research, and has all these PhDs, that’s fantastic and great, but that does not define a successful candidate, and it does not define someone being able to succeed in this industry. I have seen so many different types of profiles and I’m proud to say even being out of GA and also connecting with other different types of [inaudible 00:46:05] or someone who came from a different discipline, being able to really possess the ability to be successful in the field of data science. Again, what’s great about that is that it’s collectively creating a different type of perspective and it compounds different types of discipline into one, if that makes any sense. For example, if someone has an economical background they’re able to come from that perspective and then of course use data science or be in that field and have that type of perspective that can really apply and help in relation to that field. Or someone in a media background, or someone in a scientific background, or so forth, right? Retail background. Wherever your background is, I would say what are the skill sets, whether it be life lessons that you’ve learned or even just on the more hard skills, how is that rolling up and how are you utilizing that and threading that in to the skills that either you are collecting or have collected in the field of data science? How are you making that work for you together? I think that’s really important to recognize that.

But again, to answer that question in short is, definitely I don’t think that what defines success in data science only comes from a computer science background or a stats background. It’s not only there. It shouldn’t be limited to that because I’ve certainly seen so many types of diverse talent and when I talk about diverse talent, I mean different backgrounds, what schooling you went to, and all sorts that have really been able to become successful in this industry.

Kirill: Yeah, totally. Could not agree more. Everybody should leverage their backgrounds and they should also express explicitly how they’re leveraging their backgrounds and how that is making them unique and successful and what additional value that helps to bring.

All right, so that was a very cool insightful talk about recruiting and things like that. I’ve got a couple of rapid fire questions for you, are you ready for this?

Urie: Oh-oh, yes.


Kirill: Okay. What’s the biggest challenge you’ve ever had in your career?

Urie: Biggest challenge I’d had.

Kirill: Yeah. Probably more relating to like data science side of things, like recruiting data scientists. Like in the past seven years, what’s the biggest challenge that you’ve had or you face on a regular basis?

Urie: You know, this is going to be a fun answer and my goodness, I know my company’s going to end up listening to this, [laughter] Going to chuckle out of this.
In the span of me working in recruitment, I think the biggest challenge is that sometimes, engineering teams or who they think that they’re looking for doesn’t always map out to what they thought it was. To be a little bit more descriptive, so for example let’s say that it comes back to that job description that comes back to, oh should I or should I not apply. You think that when you’re looking for a particular talent to join the team, you think that it’s very black and white, and it’s like yes, this is exactly what we’re looking for, this is who’s going to complete this mission and feel wonderful. But that’s not necessarily the case. I mean what you thought you were or what engineering teams thought that they were looking for, actually can change. It can adapt. Meaning as they discover going through all these different profiles or especially the requirements and the role and the activity on that team, that could change over time.

And so, I think the challenges of that is sometimes when we talk about the time sensitivity of things, of trying to find that person immediately to hire and to be that perfect fit and so forth. That poses its own self time versus the ideal candidate and is that realistic and how does that really connect. Right? So again about, sometimes candidates might think, oh wow, you know I look at this job description and this person is like a super human. But on the other spectrum of things, do understand that sometimes engineering teams in the moment of what they’re working on things like that especially in a production environment, it’s high volume it’s high stress. All these things are factored into it so when they’re looking for this candidate it might look like this candidate needs to be perfect, but that’s clearly not the case. But then of course revisiting what that candidate profile really should look like, time gets lost when we’re not you know realistic with the expectations of who’s out there, who could do the job and so forth and what we’re really looking for. What really is the must-haves versus the nice-to-haves.

Kirill: Yeah, gotcha. That’s a good thing that the company that you’re with they’re flexible, that Collective[i] they’re flexible about, you know, they put out one job description and then they look through the profiles, they adapt and then you ultimately find even better candidates that way if you’re willing to adjust your job description along the way or be open to new ideas about who might be best suited to be on your team.

Urie: Absolutely, and we certainly have. And especially being in a recruitment space and any of those who might be in the HR space listening to this podcast, I get it. It is frustrating because it’s like okay we’re doing the dance, and we’re going back and forth and so forth and at the end they’re like, you know what, why don’t we try it this way? It’s like hey, we could have done that a month ago, right? But again, you have to build that kind of- Your engineering team or whatever team you’re recruiting for, remember at the end of the day that it’s very important to work together and then just continually backed by data too. Right? Say this activity this week based off this criteria, you’ll get these results. But guess what, when we decided to go from a different angle and maybe switch up the search strings we came up with this kind of results, and that result was better than last week’s. So, presenting data in that sense is very important too because it clearly really shows the teams that as well.

Kirill: Gotcha. Well okay. Next question. What is a recent win that you can share with us. Something you’ve had in your role, something that you’re proud of?

Urie: Recent win? I would say, there’s actually a few of them. I recently joined Collective[i] and so- I mean there’s many different wins. One of them is definitely being able to onboard candidates quickly and have a positive candidate experience for the new hires. To make sure that they’re happy and they felt like they were on boarded properly, that is a huge win always for us, for Collective[i] just because we always want to make sure that we’re balancing out how someone feels comfortable and they’re well received when coming into a new space. Especially when it’s like, congratulations, you’re with us now and now you’re going to learn XY and Z and all of these things, right, good luck. [laughter] So, getting them to feel that when they first felt that they were like, oh okay, I’m going to take this risk and join this very cutting-edge place or a startup and having those butterfly feelings, it actually mapped out to be felt like they made a good decision. So that’s probably one of the biggest wins.

But personally, for me I think huge wins is that, again, talking about job descriptions and working with engineering teams and so forth, I think the biggest win is for me recently is that I’m really fortunate to be able to connect the way I do even more so than ever with my data science team. I know it’s always going to be an interesting challenge to find that right fit but when you realize without even thinking about it, and you’re just doing your daily recruitment activities and there’s this trust between myself and the team, and that rapport and relatability is there, that’s priceless. Because that really makes me feel good that I can do my job effectively for them and that they give me that kind of trust that I really am considering all their points that they have spoken to me about and really implementing that when I’m looking for that candidate.

Kirill: Yeah. That’s definitely a big win. Okay, next question. What is your one most favorite thing about the work that you do? About recruiting in the space of data science?

Urie: Oh wow. So, working in AI-

Kirill: Yeah.

Urie: Is really fun. [laughter] I mean fun in the sense that it is challenging but it’s fun because it really stretches the scope of your imagination of where AI and where it’s heading can really go. So, I think to me that is something that I thoroughly enjoy and the reasons why I particularly joined this company is because while I am not fully practicing data science now coming out of the program that I took, I still am surrounded by it and I still am at my pace, have the ongoing nature where I can reach out to my data science team and be like, hey I have this thought, I have this question, and they’re always continually helping me grow and learn. I’m very fortunate that I found, I would say, a unique opportunity to do so, and still practice at the discipline that I love which is People Ops.

Kirill: Yeah, gotcha.

Urie: So yeah.

Kirill: Very, very, rare to hear a recruiter talking about their own opportunities. This is unique. I’m very excited about this. [laughter]

Urie: [laughter]

Kirill: Okay, Then I have a philosophical question to wrap this up. From what you’ve seen from all the work you’ve done, the work you do and the candidates you’ve seen and the job descriptions, where do you think the field of data science is going, and what should our listeners prepare for in order to be ready for the future?

Urie: Oh, this is a tough question. I truly feel that the title of data analytics or data analyst is probably going to go away in the near future. I think that will soon be replaced by a traditional data scientist and a lot of data scientists are now moving more into the deep learning space, and towards AI and machine learning. So I think it’s really important to always make sure that you have the ear on the ground and recognize what’s going around as far as not just what’s trending but what makes sense as far as where the momentum is going when we talk about just the world of data. What I find really interesting currently right now is that data science is almost becoming interconnected with big data. Right? I mean it all still is in the data family but I’m seeing more and more a cross pollination between the intersection of big data and data science. Right? So, there have been very interesting candidates that I can give for an example that I’ve seen that maybe they were more of a dupe developer or working more on cloud and things like that of that nature and being more on the engineering side, now migrating over to the data science field and then also same thing, I see a lot of data scientists now ramping up on technologies such as Spark and so forth, being able to really be more connected on the big data side of things. That wheelhouse.

Kirill: Yeah. That’s some good advice. So, AI and big data, things to look out for, that’s where everything is going to end. I also feel in terms of big data I also feel that there’s so much more data coming in now and the velocity, the variety, everything is increasing, that volume, that inevitably people are going to find themselves working with big data, you know, three five years from now, any data scientist can be expected to be able to work with big data, so that’s definitely something to look into as well.

Urie: Absolutely. And also, I feel like even the title data scientist, there are many different sub sectors under that title. You can go into more on the machine learning side or, you know, I feel like even a sub sector of machine learning going more into like when you talk about deep learning that is natural language processing and so forth. So, it’s going to span into much more of a specialty or specification I feel as well. Because I feel like data science is now becoming more of an expansion of a discipline or an area of technology and then there’s going to be a lot of different sub sectors where people can really practice a more horned in discipline.

Kirill: Gotcha. Fantastic. Thank you for that. We’re going to wrap up on that. Thank you so much for sharing all your insights. How can our listeners contact you, get in touch, follow you, maybe somebody might be interested at positions at Collective[i], what are the best places to reach you?

Urie: You could directly apply at You can certainly reach out to me on LinkedIn, Urie Suhr. And then I always love finding, again, connecting with all different types of talent on Twitter and my Twitter handle is @uriesuhrci.

Kirill: Could you spell that for us, just so that people know? It’s at-

Urie: Yeah, absolutely. Twitter @uriesuhrci, all one word. Then on LinkedIn Urie and then last name is Suhr.

Kirill: Gotcha. And the company is Collective[i] like i with a dot.

Urie: Yes.

Kirill: Gotcha. Okay, cool.

Urie: [i] with a dot.

Kirill: One final question. What is a book that you can recommend to our listeners so that they can become better at what they do?

Urie: Yes. I am reading Life 3.0: Being Human in the Age of AI by Max Tegmark. Max Tegmark is actually a professor at MIT and I will say that other than just enjoying the insights that this book has been providing, because it’s very incredibly insightful, it really does expand your thoughts imaginatively in what the world can become and how AI can or will proceed in our future. I’m just blown away by the simple scenarios that are given in this book but absolutely a fantastic read and I really do encourage anybody that really has a deeper interest in the AI space, please read this book.

Kirill: Gotcha. That’s a good one. I haven’t read it myself but looking at it now on Google, looks interesting. Guys, it’s Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark. Check it out and once again, Urie, thank you so much for coming on the show and sharing the wealth of insights. I feel there’s so much more we could have talked about but we’re just short on time now and maybe we’ll get you back on the show again someday in the future. Thank you so much.

Urie: Yes, thank you too. Nice connecting with you, Kirill.

Background music

Kirill: So, there you have it. That was Urie Suhr from Collective[i], she’s the Director of Talent Acquisition there, hope you enjoyed today’s episode. My favorite part was probably Urie’s comment about that connection to the opportunity is more important than a perfect resume. Something to remember and I think it’s a benefit, it’s like an advantage for both sides. Both for the company and for the person looking for a job because if you feel a connection to an opportunity you feel that this is exactly what you want to do, then you’ll be successful regardless of your resume, regardless of your background, because you’ll find ways to be successful. Whereas if you just have the perfect resume this might not really match what you do and then the company won’t get what it’s actually after, the ideal person to fulfil this role and you won’t be happy with what you’re doing. It’s always important to remember that you need to look for opportunities where you see potential for yourself to grow, to learn, to improve, and that you actually, as Urie put it, can connect with the opportunity.

On that note, make sure to get in touch with Urie. We’re going to be sharing her LinkedIn at Get in touch, you never know where these things will go, it’s always a huge advantage to have a recruiter or headhunter in your professional network on LinkedIn and that’s how I got my role right after Deloitte, through a recruiter, so it’s always good to connect with people who are looking for talent, whose job it is to look for talent.

Shoot any questions you might have to Urie, I’m sure she’ll be happy to help you out. Of course, you can get all of the show notes and the transcript for the episode at the same URL, Hope you enjoyed today’s show and I can’t wait to see you back here next time, and until then, happy analyzing.

[Background music plays]

Kirill Eremenko
Kirill Eremenko

I’m a Data Scientist and Entrepreneur. I also teach Data Science Online and host the SDS podcast where I interview some of the most inspiring Data Scientists from all around the world. I am passionate about bringing Data Science and Analytics to the world!

What are you waiting for?


as seen on: