Evan: Hello and welcome to the Mining Your Own Business podcast. I am your host Evan Wimpey, and I am excited to introduce today, April Wilson. Most of the guests that we’ve had previously have been leaders in the data science and analytics world. They sit it in an organization, they try to use data to make better decisions, and April is a unique guest. She doesn’t sit exactly in that role, but she’s very tuned in to that role. She is a career strategist and she’s worked with hundreds of companies, thousands of candidates. She’s nodding her head, so I believe that’s not an exaggeration at all. And she’s helping companies solve their data and analytics problems by putting people, helping put candidates in place there. And full disclosure, that’s how I know April, she helped put me into this position right here. So super excited to introduce April today. April, welcome to the Mining Your Own Business podcast.
April: Thank you, Evan. It’s wonderful to be here.
Evan: Fantastic. Can you start off with just a little bit of introduction about yourself – where you sit today and the work that you do?
April: Absolutely. So as Evan said, my name is April. And currently I’m the head of career services at the Institute for Advanced Analytics. I’ve been here for about 10 years. Long time, seen a lot of data scientists come, seen a lot of data scientists go, but thoroughly enjoyed my job. Like Evan said, I’ve had an opportunity to interact with thousands of graduate students that have come through the program, and then, as my after-life or what I do when I shut the doors to this organization is I also help other people find different jobs, so I have co-founded a company called Clear Path Career Consulting. And so we, me and a partner of mine, we just kind of help different people, not only just data scientists, but you know, all array of different types of people to find jobs, just to kind of hone in on the skills that they need in order to be successful in the job search.
Evan: Okay. Fantastic. April, you know, I came through the Institute for Advanced Analytics, like it’s a great school. April is probably the reason why it’s so great. But, can you talk a little bit about what skill sets people need? You know, they’re going here to a master’s program, getting a master’s in analytics. What should they be focused on? What kind of skill sets do they need to come out and, and be helpful to an organization?
April: So, in general, if you’re thinking about going into data, and you’re thinking about getting a master’s or any type of degree or certificate for education in data science. I think some of the core skills that you’re gonna need to do is it’s nice for you to come in with some type of foundation and statistics just so you can kind of understand what’s going on behind the scenes as well as having. You know, once again, some key foundational skills in some type of coding language. It doesn’t have to be anything particular. We don’t, you don’t have to have Python, you don’t have to have R, those are just some of the key ones that are out there right now, but just to have worked. And coding and programming would be advantageous, to anyone who’s coming into the program or coming into any type of data science or analytics type of program.
Going out, same thing. You’re still gonna need to be able to do that, that coding, that programming knowledge, you’re gonna, I still think the statistics are good because the statistics are gonna help you, like I said, to understand what’s going on behind that data and those programming skills and be able to help you to explain it to the individuals, your audience. But some of the things on top of the coding or the statistics that you’re gonna need as you progress in your career are those soft skills. And we call them soft, but they’re hardcore. They are hardcore and sometimes. Our data folks, I think it’s harder for them to get a grasp of those skills sometimes then it is to get a grasp of the technical skills. So your communication skills, being a good teammate, being flexible, learning, you know, how to adhere to your audience, knowing how to listen. You know, those are some of the key things that we try. Focus on and try to teach people that they’re very important as you move into being a data scientist.
Evan: Yeah. I think you’re absolutely spot on. And one of the things that has been a common theme throughout this show of people that are trying to get data science implemented is you can sometimes take the modeling and the data science part – that’s the easy part. Sure. We build a model that’s useful, we’ve got to communicate this, we’ve got to use it and to drive some change. So it’s good to see that that’s a focus of skill sets that you’re trying to cater to and trying to push forward through the institute. So it feels like most of the things you just listed were really evergreen, you know, that they’re useful for data scientists.
You’ve been there for about a decade, which is hard to believe –
April: But I’m getting younger, though. I’m getting younger.
Evan: Yeah, good, a non-linear path there. Very much.
But it’s a fast-moving field. It’s a fast-moving place. 10 years ago, if you use the same packages or algorithms from 10 years ago, you know that feels very dated now. Are there any changes that you’ve seen in the 10 years of folks coming through either the types of skill sets that they need to focus on or the type of experience that folks coming to study, data scientists coming into study data science, the skill sets that they come in with?
April: So that’s a loaded question, you know, ‘cause different companies, different organizations are looking for different things. I mean, one of the biggest things from when I first started in this job to now, is that open source. You know, having a good grasp and handle on the open source software. You know, what we are talking about are the R and the python – things of that nature. We’re having a lot, lot, a lot more companies that are asking for those skills as well as SQL, because it’s gotten to a point where now data science, in the beginning when I first started here, I don’t think people understood what it was. I don’t think companies knew the power of it and what it really means to have an analytics professional or a data scientist on their team, and so now that has been 10 years, 15 years and things have transitioned and people know the power of data, what they’re looking for in their analytics or data science professionals has also altered and changed a little bit.
So no longer are companies coming in saying, I want that coding person, I want that, you know, programming person. Like I said in my first answer, what they are asking for more now is not only people who know it, but people who can talk about it, people who can explain it. People know what’s going on behind the scenes, someone who can talk to a PhD, you know, a person in statistics or something and understand what they’re saying, and then convey that information to their customer, which could be someone who has no idea what data science is. You know, it could be, any stakeholder. It could be a CEO, it could be a manager. Someone who, like I said, sits more on the strategy side. So what I’m seeing now is that person who needs to be right there in the. Almost like a professional, a consultant, essentially, even though you’re a data scientist, you need to almost have those communication skills so that you can consult both sides of the house, the highly technical side, as well as the folks, the audience that doesn’t understand, you know, who can see those numbers, but looking like, what, Just tell me what the bottom line is. You know, do I need to increase my prices or decrease my prices? Someone who can kind of understand the business and be able to transfer their data knowledge into. Sense for the business. So I’m seeing that more, like I said, in the beginning it was more techno, techno, techno. Now it’s like, okay, yeah, I want you to know the technology, I want you to understand it, but I need for you to be able to talk about it and explain it as well.
So we’re having a lot more surprising, a lot more by customers come in and they are focusing more on those skills because they’re kind of saying, you know, I can teach the people the technical stuff. I want them to have a good foundation, but I can send them to another, you know, workshop or, or something else. So I can help them learn or increase their knowledge of the technical stuff. But April, it’s really hard to find someone that I don’t have to, you know, take them to a workshop to teach ’em how to be a good team player or how to make a, how to do a good presentation, how to talk to stakeholders. So those are some of the key skills and stuff that’s transitioning.
Evan: I think those are great points and I think that’s a very fair point. It’s almost as the industry’s gotten a little bigger. There’s more experience out there. It’s easier to leverage some of the folks that have been in data science longer. Hey, we can teach some of the technical stuff to a good team player.
I really like that you mentioned data science is almost a consultant. I do work for a consultancy we’re actually a consultancy, and even some of our previous guests do that internal to their organization. I think a previous guest Alex Cunningham, who’s at Church & Dwight, he was a guest on the show and he mentioned their analytics Center of Excellence is an internal consulting group, and they almost treat themselves as a consultancy, reaching out to their operations team or sales or marketing. So yeah, communication is very important to convey the value of the analytics team.
April: Absolutely. Absolutely.
Evan: Awesome. Well, it sounds like that is a focus point at the institute and the things that you try to prepare candidates for. Is there anything you, you don’t work just with the candidates. You work with the companies too. The companies want to solve problems, they want to bring people in that can help them solve the problem. Is there anything that they tend to focus on, or really value that maybe the candidates tend to overlook and think, I don’t need to think about that, or don’t need to worry about that, but the companies really do. That’s something that they really value?
April: Yeah. I know, I don’t want to sound like a repetitive thing, but I mean, it is just ironic to me that over and over again like I said, the companies, the candidates don’t understand the importance of being able to communicate, being able to present. And you know, they, a lot of folks that are coming in, to our program, other programs, people that I talk to when we go out to, you know, talk to other students at colleges and stuff, they focus so much on the technology part.
And that’s great. You have to know that. Don’t get me wrong, I’m not saying that those skills are not vital. You have to know those things. But like I said, I just think that the places that our, our graduates and the data scientists and analytics professionals are going into right now, they’re almost like subject matter experts. And with that being said, like myself, I feel like I’m a subject matter expert when it comes to helping people to, you know, figure out how to get a job. , you’ve gotta know different things. You just can’t be tunnel vision, you know? It just can’t be all about the technology. And so, you know, we are having a lot of companies that are coming in. Like, for example, it used to be, when I first started, you know, all your interviews were behavioral, Everything that the company felt like you needed to know, they felt like they could kind of just get out of the conversation slowly but surely, that’s transitioning now. We’re not only having people that are having conversation because they wanna see how you communicate, but they’re also bringing in presentation skills. There’s a lot of companies who will give you a data set and say, solve this problem, or what do you see? And your interview is to present it to us, so that’s not only, talking, you know, being able to, to talk through the problem, but to see how you create a presentation, how you looked at the data, what you got from it, and how you can kind of help a company to figure out why you came to the resolution that you came to.
So it’s twofold. You know, gone are the days that someone’s gonna sit down and just say, Code this. Okay, you’re hired, you know. Now it could be Code this, Let’s do a case study and figure out, you know, why you went that way. And then we want you to do a presentation to tell the stakeholders, you know, the steps that you took in order to get to where you’re going. So once again, it is, like I said, it’s just, it’s like a three-prong. It’s that technology, that communication, and that ability to present to stakeholders. And be able to present the right information to your stakeholders.
Evan: I think that’s great. And that’s, you know, from an employer’s perspective, if you’re hiring, like that’s sort of the golden goose you’ve got, you can yes, evaluate how technical they were. Were they able to find things that the business would value and are they able to communicate that well? There’s also a time and effort component to that. Do you think that most employers are doing this now, and is the expectation sort of for the candidates to, if you’re interested in the role, this is how you’re gonna be evaluated, these are the types of things that you need to do?
April: Absolutely. Absolutely. Because I mean, Evan, there are thousands of programs out there right now. You know, so you know companies, even though a lot of people say there’s a lot of data scientists out there, there’s all these programs, they’re putting ’em out – No, there’s no. There may be a lot of people who have a certificate or degree or whatever, but everyone doesn’t bring the same amount of knowledge to the table. So I think the companies are, at this point, you know, they’re getting hundreds of people to apply for these jobs that have, you know, these degrees and these certifications, but they want to hone it in on the people who are really serious and to do that, a lot of times they’re saying There’s several different layers that I’m gonna need for you to take or to get through before you are offered this position in order for me to, you know, to kind of take you seriously. And, but also what I like about that is that it’s being truthful to the candidate because you’re seeing, you know, This isn’t just a coding job. They’re gonna be expecting me to do all these different things as well. So it’s kind of giving you a little taste of what your day-to-day could potentially be like throughout that process.
Evan: Yep. I think that’s, that’s a great point. Yeah. And, and it sort of helps, helps somebody filter through if all you need is, Hey, submit a resume, then yeah, we’re back to the days of here’s a thousand resumes. You use some parser or probably some data scientist has developed a nice AI that says Pick these three, interview these three. But if you can sort of self-select the really interested candidates that are willing to put in the time to go through all of those steps and see what the job is like, I think that’s an easier matchmaking process. But it does feel like the heavy lifting is upfront. Got to do the work.
April: Absolutely. And you gotta watch out for those job descriptions as well, because a lot of times, you know, when folks write those job descriptions, they don’t know what, sometimes they don’t even know what they want, so they put everything in there, or they put something that they think they want. But sometimes you just don’t know what it is you want or need until you actually come across a person who demonstrates a lot of different skills. So, you know, watch out for the job descriptions and don’t get, you know, deflated when somebody’s like taking you through – now, five, six different levels. That’s one thing. But I think it’s very normal to expect nowadays to go through at least three, you know, maybe iterations, talking to different people. As long as you are, you’re progressing through that. And like I said, that’s gonna give you a little bit better understanding of really what they’re looking for, because you want to go into that situation knowing that just as much as they want to go into the situation, knowing what they’re getting. Sometimes you can read a job description. And you’re like, Oh, I can do this job. But then you go through the interview, you’re like, That’s not what it said, you know, Why are you asking me to code this? It didn’t have anything to do with it. And it’s because, you know, the person, maybe they didn’t quite know, or it could be a job description that they wrote 10 years ago that they’re still using now.
So, yeah, it is a give and take, but a lot of employers are using, you know, all these different types of levels to make sure that you are indeed interested and have the skills that they need.
Evan: Perfect. Yeah. I don’t know of what kind of company would use an outdated job description when they’re looking to fill a position.
April: No, I don’t know of many companies that do that.
Evan: We’re working on it. We’re working on it. April. This is great.
So there’s a cohort at the Institute for Advanced Analytics, a hundred ish people that are there for each 10-month-long master’s program. You get, you get folks that are just out of undergrad that are young. You get folks that have a little bit of industry experience. You get folks that have had basically a full career and then come back in, cuz they can’t stop learning and they want to keep learning. So is there a difference in the way you try to help those candidates with varying levels of experience? Or the things that they should be focused on?
April: No. no. There’s not. I mean, you know, I, I’m not someone who comes in with – First of all, a lot of times the people that come through the program don’t have a lot of experience, even if you have a lot of experience, professional experience, they may not have experience in data science or analytics. Certainly. So with that, we don’t normally get people that come through the program normally that have five plus years of data science or analytics experience. So with that said, the way I treat the two individuals is actually the same when I tell them. The biggest thing is to stay open. Because we all have to admit when we’re launching ourselves into a new career, we really don’t know what that career. We don’t know where it’s gonna take us. And you know, for my experienced folks, there may be some things in your past that you are really, really good at that you wanna bring along with data science. So, you know, you have to think about that. So maybe your first job, you may wanna really focus on the technical aspect of it, just so you can kind of understand it and get a knowledge of it. but then once you get down, there may be some things that keep coming back to you. You know, you are a good communicator, you know, or you are a good presenter. You did something really, really well that you want to marry into that data science background. And so, you know, those are the, that’s the biggest things when it comes to the junior and the more experienced people. It’s not the way I tell you to look for a job, the types of jobs – it’s just a matter of being intentional.
You know, when you come straight outta undergraduate, you’re kind of like, I just, I’m open, I’ll do anything cuz I don’t know, I don’t have any professional experience. , so those folks tend to, sometimes it’s easier to kind of get them to be open and say, you know, let’s look at a residency model where you go to this particular company. You stay there for two or three years, you kind of get an understanding of what data science is. You start learning what you are good and what you are not good at, and then when you move on to the next company, you kind of make a list of that and so you know what you’re looking for and what you’re not looking for. Whereas my experienced people, they already may know some of the things that they are and are not looking for, but a lot of times what they’ll tend to do is their first job. Out of any type of, uh, program. They may want to, like I said, really hone in on their technical skills. But after you get a year or two of that, you know that you’re good at it and you kind of get the fill of things. Then, like I said, you want to find an opportunity that is gonna marry that, those technical skills along with the things, other things that, that you’re good at. Maybe some of those, you know, like I said, communication skills or things of that nature. So, Those folks. Sometimes it just depends. Whatever’s most important for them, if it’s important for them to get technical, I do the same thing as I do in undergraduate.Just be open. But sometimes they’re really adamant about – April I was a teacher and I really want teaching to be a part or communicating to people, helping them to understand to be a part of that job. So it kind of helps me to kind of help them to hone in and say, Well, you know, this is the type of job we probably want to focus on right now if that’s something that you really want to do.
So that’s really the difference. There really isn’t any other big difference that I, that I look at. I don’t think that a lot of companies are not necessarily looking for senior-level people more than junior-level people. My younger people always think companies are looking for senior people. My senior people always think that companies are looking for junior people, which is a misconception. They’re just looking for good people. That’s it. They don’t care if you’re straight outta undergraduate or you know, you’ve been a college professor for 15 years, as long as you have the skills that they need and you fit the organization, you feel like you have something to contribute – The companies that I work with don’t really care.
Evan: Yeah. I think that’s great. And yeah, that’s, I’m sure it’s not just seniority, but the experience always. The other candidates have it so much easier, they’re looking for other candidates. Yeah.
So not many folks are coming in to get a master’s that already have substantial data science experience. But there are several. It have maybe some industry experience, maybe they’ve got a background in banking and finance or in agriculture. Are there, are there any big trade-offs? Like is it, is it particularly useful to go back into a field where you have some industry experience? And I’m thinking about from an organization’s perspective too. A lot of folks will hire or train within. So does the banking team that wants to hire data scientists, is it useful for them to grab folks with banking experience?
April: No. It is ironic that you ask that because, I find that, mind-blowing, but we don’t, I think I get the opposite more so I’ll get my financial institution. My financial institutions will come in and they’ll see someone with that has that background. They don’t just go towards their person automatically. I mean, it, like you said, it’s fresh thoughts and it depends on what you did in the banking industry. So, you know, if you were like in the mortgage industry or something of that nature, may, if you go into the data scientist side of the house, depending on what department it is, and they have nothing to do with mortgages. So just because you’ve worked at a bank before doesn’t necessarily give you. You know, a step up or, you know, or more leverage with those types of organizations. As a matter of fact, I have some companies, that will actually say, no, I know that company and I know how they train their people, and that’s probably not the way we want to go. And it’s harder sometimes to break, you know, to kind of get people to open up and to do something fresh. So, actually no, I don’t get that.
But also, I don’t very often get people who want to marry data science in their old careers. If someone did something extremely specific, like, like you said, work in, agriculture. You know, that’s very, very specific. Or they’ve done something like work in the pharmaceutical industry for some reason. You know, things like that, people do try to stay there because those skills may be transferable and I think it’s hard to find a data scientist that, you know, someone who has data science skills as well as those skills, but normal things like banking, marketing, you know, things of that nature. No, they’re not necessarily looking for someone who has that skill set. You know, they feel like I can teach that. Those are the things I can teach. April, I want someone who comes in with a good understanding of data science, and then I can teach them the other stuff.
Evan: It is a surprising answer, but I think with a layer of depth, it really makes perfect sense. I think as, as consultants, either internal or external, like one of the hardest things to overcome when you’re trying to say, We found this new insight, we found this new thing that is in the data, it’s, well, but that’s not the way we’ve always done things. So yeah, somebody who’s come from 10 years of industry experience, they’re sort of already married to that way that they’ve already done. Data science is looking for fresh, new insights where can we make better decisions.
April: Exactly. And not only that, if you come from that industry, people tend to think that they should go in at a higher level. It’s like, okay, but you didn’t do data science; you did something else. So that doesn’t necessarily mean you’re gonna get a managerial role, or you’re gonna go in at a higher level. You’re still probably gonna go in at the same level. Maybe, maybe a little bit higher than the person who came shot outta undergraduate. But most of the time it’s, you’re not gonna go in there. Here it might be, you might go in right here just because you have a specific knowledge, but only if that knowledge is needed in their organization. So that, Yeah, that’s another thing that makes people, they, it’s kind of like, Whoa, well, I’ve worked at this company or in this industry for 10 years. I think I should go in at a higher level. And they come out with essentially almost the same offer as one of our undergraduates or someone with one or two years of experience. But it’s, like I said, it’s because the knowledge that you have really doesn’t bring a lot of value to where you’re gonna be as a data scientist.
Evan: Certainly. Makes perfect sense.
So April, I do want to ask you one more question. A lot of our audience here on the podcast are folks that are managers and leaders in an organization that’s trying to implement the data and analytics solution. So a lot of these teams are hiring. Knowing that you’ve got them on the other side of these speakers, what advice would you want to give to them? How should they think about hiring for a team and looking for candidates?
April: Know what you want. Go in and be able to communicate that to the person who’s applying. Know what your expectations are to be fair to the applicant so they can kind of say, I can do this, or I don’t think I’m ready for this right now.
Second is: Remember, there’s no such thing as unicorns, you know? It takes a team, a data science team to do well. So, you know, gone are the days where you can hire one person and that person does everything. Know that a lot of times successful data science departments have at least three folks. And that could be, like I said, someone who’s your data engineer, maybe a data scientist. And then maybe someone to come in to kind of help pull all that together. So, those are the biggest things.
But my last and final thing is just to be open. You know, don’t go in saying, you know, I’m willing to talk to a lot of different people at a lot of different stages in their life, because you never know, you know, who’s gonna be that great fit. So when you kind of open yourself up to kind of say, I think I know what I need, but I’m not sure. So, you know, if this person right here, normally I wouldn’t have talked to them because they don’t have a certain skill or they don’t have the certain years of experience. They’re intriguing, you know, Or they’ve reached out to me or they’ve shown a lot of interest in this organization. Talk to these folks. Try, try. You know, try and see, give them the opportunity because there’s always, every year, like this year, we have 90 some odd diamonds in the rough. And when you’re recruiting out of a program, especially outta educational program, realize that these individuals are young in data science, no matter how old they are in age. And so sometimes their expectations are just as skewed. And your expectations, you know, can be. So that’s why I just say be open and you know, really pay attention to those individuals who are making the effort to reach out, try to get to know you, try to get to know your organization. Sometimes, you know, just being a mentor to a person or having a conversation with a person that’s not necessarily about the job, you can learn a lot about those particular individuals.
Evan: I think that’s a great point, your last one. And I think that ties in a lot to the soft skills that are also very hard when somebody reaches out, when they’re trying to communicate, when they’re looking for mentorship. Like they’re showing you their communication right there. So, to see how they will communicate in your organization.
Evan: April, thank you so much for coming on the show today. It’s been a pleasure talking to you. I think that our audience will get a lot out of this audience if you are interested.
They’ll be entertained at the very least. Audience, we will leave a way to contact April in the show notes page if you want to reach out to talk about your career, or things that April might be able to help you with. If you enjoyed this content, please subscribe or like, or do whatever the things on the podcast things so that you get the next one coming up too. April. Thanks so much.
April: Thank you, Evan. It’s been a pleasure as always.