Transcript
Speaker: Hey there, everyone. Welcome back to another episode of Life After Tech Bootcamp, the podcast where we dive into the stories of tech bootcamp grads and see how they've landed into the tech industry. By now, we've heard a ton about how our guests have used their past experiences to score tech jobs. But let's face it, not everyone's got a career history to bring to the job market. And yes, I'm referring to recent grads.
Speaker: I can completely relate to being a fresh grad back in the day. I had interned my entire senior year doing assistant level tasks, in my opinion, yet every interview felt like a broken record. They would tell me I lacked the experience for an entry level job because I was only an intern previously. It was incredibly frustrating, especially taking the train from Philly to New York and having that told to me within 30 seconds and ending the interview right there.
Speaker: So moving on, I'm not bitter. Sure, experience like that is still gold, but even then it sometimes feels like it's not quite cutting it. And I can't imagine anyone who's never been annoyed by those entry level jobs demanding four years of experience. The job searching paradox is so frustrating and unfair and the whole needing experience to get experience, but how do you break the cycle? Well,
Speaker: We are going to talk about just that today. And I'm so excited to introduce you to our guest Cole. Cole began his data science course after completing his undergraduate degree in cognitive science. And he's currently working as a data scientist at the National Research Group. Hi, Cole. Welcome. Thank you so much for being here. Hi. Yeah, thanks for having me on. Yes. And where would you like to share where you're recording from today?
Speaker: Yeah, yeah, absolutely. I recently actually moved to San Diego, so it's nice and sunny here today. I'm like you, I'm sure, being located in the Northeast. Yes, it is the opposite of nice and sunny. It is bitterly cold and getting dark at four, so definitely jealous. I've been to San Diego once before and I loved it. I should get back there at one point.
Speaker: Yeah, absolutely. It's a great location. Yeah. Well, anyways, okay. So you just got to San Diego. Clearly you're doing great things with your life, but let's go back to the beginning. You were in college. This is a bit different than other springboard students who came from a previous career, but you did this right out of undergrad. Yes. Yeah, that's right. Yeah. So I actually graduated in the spring of 2020 right at the beginning of the pandemic.
Speaker: and was really entering a job market that was
Speaker: not really, you know, there wasn't many opportunities for entry -level candidates, especially in, you know, competitive field like data science. And so, you know, I'd only had like basically one or two experiences while I was at college doing similar work. And so I was just looking for opportunities to get that experience, you know, without being able to get an entry -level position.
Speaker: Certainly. And with cognitive science, forgive me for not knowing my sciences. When I read that, I kind of thought that was something like a future therapist might get. Am I accurate on that? Or does that actually relate to data science? That's a good question. So in short,
Speaker: Cognitive science is basically the scientific exploration of the minds, if you will. It's an interdisciplinary field incorporating or drawing upon a number of disciplines like psychology, neuroscience, philosophy, etc.
Speaker: So, you know, cognitive scientists are curious about how the mind works, functions and behaves, you know, asking questions like what is a mind? You know, how does, you know, your mind enable your conscious experience?
Speaker: You know, and many, many other questions, you know, could other non -human entities have minds and what would be sufficient to create a mind and say a machine, for example. But yeah, it was a really fascinating time getting to kind of explore those questions at an institution that actually had one of the oldest cognitive science departments. So I was really fortunate to be able to work with a bunch of esteemed professors there.
Speaker: That does sound really interesting. I would have no idea how to answer what is a mine. That question would just send me through a loop. But we don't have to talk about that now. You mentioned that you had some experiences in college. Would you like to share what those were?
Speaker: Yeah, absolutely. So I was fortunate enough to be able to work as an assistant to some of the research or some of the professors in my department who were conducting research in the field. And that was really my first introduction to statistical analysis. And yeah, I guess in the vein of data science work, we were conducting experiments and using statistical models to
Speaker: to describe some of the patterns that we're noticing in the data. And yeah, it was, I don't know, one of the first things that I really got excited about, thinking about, oh, maybe I could do this as a career eventually. And yeah, that was kind of what introduced me to data science and got me interested in the field at large.
Speaker: That's fascinating. And, you know, still something worthwhile for experience. Like it kind of sounded like you fell down a rabbit hole a bit. Yeah, yeah, absolutely. I mean, yeah, you know, my experience was great in terms of, you know,
Speaker: Introducing me to a lot of these like skills and techniques And obviously the you know material the data that we were working with was really interesting to me I think the one thing though that I learned, you know at the accumulation of that experience was just that It at least in academia personally, I felt like it was a struggle to Work at the pace in which you know
Speaker: one works, you know, having to really like, dot your eyes and cross your T's and be very thorough and, you know, get
Speaker: peer reviews and, you know, sometimes papers don't get published for quite some time. And I just wanted to kind of move at a faster pace. And so that was what was really interesting to me about data science and industry, because there's, you know, it's such a fast moving field.
Speaker: And really like at the forefront of innovation for a lot of, you know, especially for a lot of these tech companies. And so, you know, I was really excited about trying to get involved in that kind of environment. Certainly, certainly. And I have heard academics, it is quite rigorous. And I have noticed a lot of people have shied away from it.
Speaker: But you wanted something more fast -paced. What was the connection you made? You're working in these experiences. You're discovering how data is being analyzed. What was the link between looking into data science professions? Like how did you start to research that? Oh man, it was so long ago now. I think I, you know, I think I was in touch with a couple of the
Speaker: alumni from our department and recognize that there were some people who, you know, studied the same degree as I did and, you know, ended up going into data science. And so, you know, once I was introduced to that as a possible career track,
Speaker: Yeah, I kind of just wanted to learn more about it. I wanted to see what kinds of jobs are out there in that industry, of course, but also what kinds of industries are incorporating data science in their work.
Speaker: I came to find that it's such an exploding field, really, that most all industries are, in some way, shape, or form, trying to incorporate some of these newer, advanced statistical techniques into their work.
Speaker: The other thing I really was kind of yearning for, if you will, after completing my undergraduate degree was just getting some more hands -on experience doing those kinds of projects. In class, we talked a lot about theory and we spent a lot of time answering those kinds of complicated questions, some of which I mentioned earlier. But I was really curious about how to
Speaker: to really employ some of those ideas and build things with the tools that were at our disposal. And so that's actually really how I came across Springboard. And I saw that as an opportunity to, like I mentioned earlier, get some more hands -on experience. Certainly. And as you were describing this, I was like, man, it sounds like Cole wanted to build something.
Speaker: talking about theory for, it can be fun. I love it, but there is a difference between a thinker, I think, and a builder. But you answered that. So, okay, so you're wanting to build something. You've found springboard. I know you said in our pre -interview that you were really proud of the capstone project that you built. And I would love for you to share that story.
Speaker: Yeah, absolutely. So I came across a dataset specifically working with the Spotify API. They have a bunch of interesting data points about subjective features of music, such as a song's valence, like whether it's happy or sad or it's danceability. Yeah, there were several others, but it was quite interesting.
Speaker: what kinds of subjective features of the music that the data sets describe. So I wanted to see what I could do with that and I eventually decided to see if I could classify a song's genre based on these features of music. So I trained a classification model and it didn't have
Speaker: the greatest accuracy as there's a lot of overlap between these kinds of features and certain genres. And so I eventually, I believe I took the artificial intelligence and machine learning track within the data science course. So I was learning about
Speaker: some of those more advanced techniques such as natural language processing and ended up employing some of the things that I learned there to calculate the sentiment of a song's lyrics and adding that as a feature to the model which improved the accuracy significantly. So that was a really cool project kind of getting to build upon, you know,
Speaker: in kind of a rough draft or a prototype and seeing where I could access more information that would get at what I was trying to accomplish at the end of the day. Certainly. Yeah, I would imagine that would be really challenging. I was reading a little bit about Nirvana and their music history and
Speaker: I'm not a music aficionado, but yeah, you would on the surface classify them as rock, but then it's grunge, but then curcumin really wanted to pop sounds. So I wonder, I'm just out of curiosity, did you classify it by maybe like rock and then have sub genres below it?
Speaker: Yeah, so I did keep the categories of genre at a fairly high level, just for simplicity's sake. But yeah, I mean, you could certainly take that idea and run with it. There's so many subgenres of music these days, and each of them are really unique in and of themselves.
Speaker: Certainly. Very, very cool. Well, so that's what you did through springboard. Um, was there anything specific within the course that really helped you that you're employing today? Yeah. Um, yeah, that's a great question. It's interesting because, um, the, the first job that I landed after graduating, um, actually I didn't really employ most of the skills that I learned in springboard.
Speaker: We had a specific set of software that we used at our company, and the job's responsibilities were distinctly different from that of a traditional data scientist. It was more of a junior -level data analyst role. But since then, I'm actually
Speaker: I've actually taken up the responsibilities of the data scientist role at this company, and I'm using a lot more of the skills and techniques that I learned at Springboard, which is really exciting.
Speaker: So that sounds like a very coy way of saying you got promoted. Yes, that's right. Well, congratulations on that. We had another episode previously talking about career progression and just having the patience to maybe not do exactly what you think where you took an analyst job and eventually growing into data science. So I can't imagine how rewarding that feels.
Speaker: Yeah, it's definitely very rewarding. Yeah, thank you. Absolutely. And so did you do anything outside of the course to really prepare you? At the time, I did maybe one or two other courses specifically, actually, with wanting to learn more at the time when I was working on my
Speaker: capstone projects, I wanted to learn more about natural language processing. And so I found another course, more of a hands -on walkthrough of how to implement some of those kinds of techniques that was really helpful in finishing that project up. But yeah, I did do my best to really just stick to the curriculum. And I knew that in such a short amount of time,
Speaker: I knew that I wouldn't really be able to master any of those skills and that I really was going to have to continue to work with those things and learn more, especially like I mentioned earlier as this industry continues to evolve. But yeah, I found it easy to get distracted and so I learned
Speaker: very quickly that it was best just to try to power through the course and complete it in one fell swoop. Certainly, certainly. So you're finishing up the course and you're starting the job hunt. What was that like? Yeah.
Speaker: So, you know, I really, I really enjoyed working with the career counselors. I think, you know, part of the struggle prior to, you know, to joining the springboard bootcamp when I was applying for jobs post -graduation was that I didn't really know what I was doing. You know, I didn't have
Speaker: Any affirmative feedback like oh, this is you know a good resume. This is a good cover letter You know to use it the right kinds of jobs to apply for and all that kind of stuff I was really just shooting in the dark
Speaker: And that can get discouraging, especially when you don't hear back from a lot of these jobs, given it was a poor job market at the time, especially for entry -level candidates. But that was really helpful to be able to work with the career counselors in my experience and have a certain expectation as to how I should go about the job process.
Speaker: They provide some guidelines as to how many jobs you should apply for on a weekly basis and how many networking connections and et cetera. I reliably met those or exceeded them. And yet, for the first couple of months, I still really didn't hear back from many of the jobs or at least didn't land an interview.
Speaker: But, you know, I was ensured that it's a high volume strategy and just to have faith in the process.
Speaker: I think they at the time informed me that something like 95 or so percent of springboard graduates land a job within six months. I don't remember if that was the exact statistic or if that statistic has changed, but that was really encouraging knowing that this process works for almost everyone who graduates the program.
Speaker: I kept at it and eventually I did land a couple of interviews, one of which with the company I work for now. And interestingly enough, I wasn't aware of the job opportunity prior to
Speaker: Prior to applying, I was actually contacted by a recruiter who was employed by the company. And he had actually seen my LinkedIn profile, which the career counselors helped me cultivate in order to really describe who I was and what kinds of opportunities I was open for.
Speaker: Yeah, it was definitely, definitely a great experience. Fantastic. So I'd love to know, well, I want to know a lot of things, but first question, did you apply to jobs after you finished undergrad or what was that gap between like,
Speaker: I did, yeah. I spent the bulk of the summer applying to jobs. And like I mentioned, just did not have any luck whatsoever. And so that was really one of the motivations for seeking out a program like Springboard, especially because I wasn't in a position to go back to school, get a graduate degree. And it seemed like a great alternative
Speaker: As well as, you know, being much more of a hands on learning experience, you know, a project based curriculum, which was, you know, that was something I felt like I was lacking during my undergraduate degree. So, yeah, that was kind of.
Speaker: you know i i think i may have even set like a deadline for myself like okay if you know if you don't hear back or get you know certain number of interviews and the certain number of time then maybe consider like you know going and um accumulating more experience to add to your resume.
Speaker: Certainly, certainly. So great point experience, right? You know, coming out of undergrad, you don't have years of a career experience. So what did the coaches kind of help you with to really bulk up your resume to say like, Cole can do this job?
Speaker: Yeah, I mean, of course, like adding springboards, the resume, it was the first thing that we did, but also not just, you know, the fact that I had graduated the bootcamp, but also some of the specific projects that I had worked on as well. And those were
Speaker: It ended up being major talking points in the interviews that I did land because they had seen that I was able to do the job that they were hiring for. I wanted to know what my experience was doing that. It wasn't necessarily something that I realized you could do, I guess.
Speaker: uh, joining springboard, I just kind of assumed that, um, you know, under the experience sub header, it had to be actual, you know, paid positions, but, um, that's not necessarily the case. And, and, um, that was definitely, uh, a huge, um, added bonus to, to the resume. Certainly. And I'm curious, I know there's a bit of a stigma with boot campers in general. Um, people complain that.
Speaker: they just have cookie cutter portfolios, projects. I know that's definitely the case in UX design. I'm curious to know what that's like for data science. But you said that those projects were major talking points. So would you be able to elaborate on that?
Speaker: Yeah, absolutely. That's a great point. And I think that can happen. But, you know, especially when I was choosing my capstone projects, I was advised to pick something that I was interested in, maybe something that even was related to my background, for example. And I found it easy to
Speaker: Talk about those projects and interviews simply because I'm a music fanatic, you know, so I find that to be
Speaker: something that I can really not only be passionate about, but clearly describe the problems at hand and the solutions to those problems. In my case, I was curious about classifying genres of music, given this data set and trying to find a more accurate
Speaker: information to describe this music in order to accomplish that task. And so, yeah, I would recommend to any boot campers out there or others who are just working on personal projects to pick something that they're interested in and have a problem that they're passionate about solving.
Speaker: Certainly. And that definitely is an extra flair to make you just sound more passionate about the job you're interviewing for. Yeah, absolutely. I mean, I also find it, you know, I totally agree. And I think I also find that it is
Speaker: Easier to get invested in the project in and of itself if it's something that you're interested in, if you're motivated to solve the problem at hand.
Speaker: Whereas, you know, if you're working with one of those cookie cutter data sets and, you know, I found that they all have cookie cutter projects for UX too. And I see the same projects over and over again. And there's only so much you can do with something that's somewhat pre -made. But going back, what else helped you with landing a job?
Speaker: I mean, in my case, the job that I landed, you know, like I mentioned, was due to the fact that the recruiter reached out to me. So, you know, I may have underestimated it at the time, but really curating your LinkedIn profile to
Speaker: really communicate like who you are and what kinds of opportunities that you're looking for is certainly an important thing. I found like networking really helpful as well, you know, and not just, you know, looking for referrals to, you know, openings at their companies, but more so just learning about their experience. And what I found is that everyone has, you know, really a unique journey that led them to their, their position, their current position or, you know, that
Speaker: guided them along their career growth and career path. And yeah, there isn't necessarily one universal, you know, set of steps that everyone follows in order to land where they would like to necessarily. And so really just being open -minded to opportunities and
Speaker: Yeah, staying connected with people in the community. Certainly. So tell me about this recruiter that reached out to you for the job that you have now and you've also gotten a promotion in. What was that experience like? You know, I actually moved pretty quickly. I was waiting to hear back
Speaker: from another interview when I was contacted by the recruiter. And he told me a little bit about the company, said that they were looking for someone with my skillset to join the data science department. And that if I was interested, then he could set up an interview for me. And so I ended up interviewing with my manager as well as the head of the department
Speaker: after which I got an offer. It all happened very quickly. I'm sure that's the case for many others in the job searching process, but I ended up feeling really good about it. The company is really interesting to me. I love the work that I do and the kinds of problems that we try to solve. It just felt like the right opportunity for me.
Speaker: Certainly. So I'm confused because when we talked about what skill sets you're using in your current job versus what you learned at Springboard, you said a lot is different. So I'm curious to understand what the recruiter meant by we need your skill set, but then you've learned completely new skills on the job. Yeah, so
Speaker: Yeah, that's a great point. So really, you know, at the end of the day, a lot of the things that I was doing in that junior level analyst role was quite similar to
Speaker: you know, it was the same kinds of analyses, if you will, that, you know, we were using or we were performing in the bootcamp at Springboard, just we were just using different tools. So while I was, you know, at Springboard, we learned how to implement these kinds of analyses and train these kinds of models and
Speaker: In Python, in my initial role, we used some statistical softwares to conduct these analyses, as well as some newer analyses that were more specific to the market research industry. Interestingly enough, when I was in my interview with my manager at the time,
Speaker: Um, she asked, you know, Oh, well, have you ever used these programs or have you ever, you know, conducted these, these analyses? And I said, you know, I, I haven't, I actually haven't even heard of them before. And she was like, Oh, okay. Well, would you be willing to, to learn them? And I was like, yeah, of course, you know, I'd be happy to learn them.
Speaker: And really, I think that that willingness to continue to learn really went quite a ways for me, at least during that interview and into my job as well.
Speaker: You know, I didn't necessarily have to have it all figured out by the time I graduated springboard. It was it was, you know, only a six month long course. There was no way I was going to be able to master all these skills and other skills that were specific to this industry that I was entering. So, yeah, I think I really tried to employ that mindset.
Speaker: you know, throughout my career as short as it's been thus far. But, you know, it was certainly helpful for me to end up landing the data scientist role recently. I think like Springboard's philosophy, or at least I remember
Speaker: a representative from the company talking about this at one point. I forget where it was, but I think their philosophy is essentially to have you do the job that you want to do before you are doing that job. Like I mentioned earlier, it's a project -based curriculum.
Speaker: And we're employing a lot of these analytical techniques and training a lot of these machine learning models and whatnot that we would eventually be doing in the jobs that we wanted. And so I kind of took that philosophy to heart at my job and ended up trying to get involved. Once I was a data analyst at the company that I'm at currently, I did try to get involved in
Speaker: in whatever ways that I can with the more stereotypical data science work. And that ended up being not only a great learning experience for me, but really a catalyst for to end up moving into that role eventually. Certainly. And I think my favorite part of that story is that your manager actually wanted to teach you stuff.
Speaker: And in my experience, I found that there's certain things that can be taught, but a lot of the times if you don't know a certain program, you're next. So what was that like, you know, getting the opportunity to learn with your manager? Yeah, absolutely. Um, it was, it was, um,
Speaker: not like something that I learned right off the bat by any means. I was fortunate enough to be given opportunities to conduct some of these analyses that were newer to me, make mistakes along the way, be corrected.
Speaker: And a lot of that, you know, it was self -taught, you know, there are obviously resources online, but also it wasn't like someone was like walking me through that process. Like I mentioned, you know, really that willingness to learn is something that, you know, comes from within, right? No one can force you to do it. So, yeah, that was, you know, I ended up picking it up
Speaker: eventually and I was in that role for, let's see, I think about a year and a half. So I learned a lot along the way. And a lot of that experience, I didn't obviously get to learn when I was at Springboard. It's a lot of domain expertise for the industry that we're in, but also learning how to work within a corporate environment.
Speaker: learning how to satisfy your client's needs and expectations and how to meet the expectations of your stakeholders internal to the company and communicate a lot of these more advanced analyses in simplified terms and all these kinds of things that
Speaker: Um, I really appreciated getting that experience, um, because, you know, I, I was able to learn how to do those, uh, you know, learn how to acquire some of those soft skills and, um, that, you know, I, I continue to use today in my new role. That's fantastic. So just about your new role, what's your day in, day out like? Yeah, absolutely. So, um, you know, we, um,
Speaker: I guess I should start by saying, you know, National Research Group is a, you know, we're a global insights and strategy firm. We work in the market research industry and with clients primarily in the entertainment and technology verticals, you know, some of the leading Hollywood studios or streaming providers or social media companies, et cetera.
Speaker: And we do custom research, both qualitative and quantitative for these clients. And so as a data scientist, I'm working primarily with the quantitative research data, which we collect via surveys.
Speaker: I'm using this data to build predictive models, classify target audiences, and even develop internal tools to assist some of our client -facing teams to help deliver actionable insights to our clients.
Speaker: insights about what their consumers' needs are or what their brand strategy might be, where they or their products fit in amongst the competition and what sets them apart, all kinds of different questions of that nature. So yeah, it definitely changes from day to day, but yeah, it's really exciting to be a part of.
Speaker: Certainly. So do you work with one client at a time, your team, or are you managing all the different clients at once?
Speaker: So in my previous role, I was working a lot more as a data analyst. I was working with several clients at the same time conducting some of the advanced analyses for their custom market research. Now, as a data scientist, I'm actually working with some of our syndicated products, some of our recurring surveys that, you know,
Speaker: keeps a pulse on some of these different industries that our clients are in and that we're interested in in general. So I'm not necessarily working with clients as much anymore and rather kind of assisting some of our other client facing teams in whatever way that we can.
Speaker: Certainly. So what would you say is your favorite part about your job? That's a great question. I think I really love the fact that in market research in general, and specifically on my company, we get to have our hands in a lot of pots between all the different clients that we work with, all from different industries, as well as
Speaker: all the different things that we get to do from day to day basis. We're a relatively small department at the company. Our main product isn't necessarily artificial intelligence or machine learning. The data science department is more of an auxiliary function to the larger company.
Speaker: And so, you know, as I'm, as the sole data scientist in our department, um, I ended up like helping out with some of the responsibilities that you might describe as falling under the scope of, um, data engineering or
Speaker: of, you know, MLOps even. And really getting opportunities to learn about all these different kinds of skills and sub fields within data science, if you will, has been really exciting to me as well.
Speaker: That's very cool. Yeah, I think that goes along with your willingness to learn and being able to have your hands in different parts of the scopes. With that, I'd love to understand, you're in this great job, you've just got promoted, where do you see yourself going next? Yeah, it's definitely a good question.
Speaker: I'm not totally sure. I think I'd love to continue to soak up all the learning experiences that I'm getting currently, really in efforts to become more of a full stack data expert, if you will.
Speaker: And yeah, I mean, I think I'm really happy where I'm at currently. Like I mentioned earlier, I love the work that I do. And I feel like I'm continuing to learn on a daily basis. But yeah, who knows where the future will take me. That's fantastic. And I think it's totally fine to not know.
Speaker: I mean, it didn't sound like you knew you're going into data science when you were in college, you just discovered it. And I think that's what that willingness to learn really helps carry you through. So it's, I think it's okay. Yeah, I can agree more. Certainly. Well, since we're coming up on time, is there anything else that you didn't get to talk about that you really wanted to share? Not necessarily. No. I mean,
Speaker: Yeah, I don't know how traditional my experience, my job search was, but if there's any advice that I could share to people who are in that process currently, or perhaps in the middle of the bootcamp, I would just say to have faith in the process. And yeah, to just be open -minded to whatever opportunities may arise.
Speaker: Certainly. And I think it's funny that you say traditional job search process. I feel like a lot of guests have touched upon that, whether they spoke about that in their episode or just to me privately. I don't know what the traditional job search would look like. I don't know what that is. So if somebody has a definition for that, please let me know. I would love to talk to you about it. Well, fantastic. Would you be open to listeners connecting with you on social media?
Speaker: Absolutely. Yeah. Feel free to find me on LinkedIn. Just as I reached out to many springboard graduates when I was enrolled in the bootcamp, I'm always welcome if people want to reach out and chat. Certainly. And Cole, would you be able to share the spelling of your name so people aren't adding the wrong Cole? Sure. Yeah. It is C -O -L -E. And then my last name is L -A -N -D -O -L -T.
Speaker: Well, thank you so much for sharing your story and for all your time today. I think this is definitely insightful for people who don't necessarily have a background to leverage when switching careers. Sometimes you just need to get started. And I did want to share for anyone listening, if you have any questions for Cole or myself that could be answered on a future episode, please email me at alumnipodcast at springboard .com.
