Alter Everything Podcast
A podcast about data science and analytics culture.Podcast Guide
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In this episode of Alter Everything, we chat with Avery Smith, founder of Data Career Jumpstart and host of the Data Career Podcast. Tune in as we discuss Avery's journey from a chemical lab technician to a data analyst, his unique SPN method for breaking into data careers, and practical advice on learning skills, building portfolios, and networking. Avery shares inspiring career pivot stories and insights on how to leverage AI and other tools in the data analytics field.
Panelists
- Avery Smith, Data Scientist @ Data Career Jumpstart - LinkedIn
- Megan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedIn
Topics
Transcript
Ep 184 Data Careers
[00:00:00] Join Us at Alteryx Inspire 2025
[00:00:00] Megan Dibble: Hey, Alter Everything listeners, we wanted to let you know that you can join fellow data lovers, analysts, and innovators at the Alteryx Inspire 2025 conference. It's the analytics event of the year, happening May 12th through the 15th in Las Vegas. Head over to alteryx.com/inspire to register now.
We would love to see you there.
[00:00:26] Introduction to Avery Smith
[00:00:26] Megan Dibble: Welcome to Alter Everything, a podcast about data science and analytics culture. I'm Megan Bowers, and today I am talking with Avery Smith, founder of Data Career Jumpstart and host of the Data Career Podcast. In this episode, we chat about his career journey, his advice for those looking to move into the data field, some crazy career pivot stories, and more. Let's get started.
Hey, Avery, it's great to have you on our podcast today. Thanks so much for joining. Could you give our listeners a quick introduction to yourself?
[00:01:00] Avery Smith: Yeah, of course. Thanks so much, Megan, for having me. I really appreciate it. Hi everyone. My name's Avery Smith. I am the host of the Data Career Podcast, so I also host another podcast about all things data analytics and breaking into the data field.
I am the founder of a company called Data Career Jumpstart. I help people start their data careers, specifically people who are pivoting from another career. And I've had the opportunity to do lots of cool jobs in my life, including the one I have now. But in a former life, I was a data scientist at ExxonMobil and a data analyst at a small biotech called Vapor Sense. I've also done some fun consulting things. I've worked for Harley-Davidson. I did an internship with the Utah Jazz. I taught at MIT for about a year, so I've had the opportunity to do lots of really fun data things, and I'm stoked to be here and be able to have this conversation with you.
[00:01:51] Megan Dibble: Yeah, totally. I'm excited too, especially hearing about your tips and tricks and knowledge on all things data careers.
[00:02:00] Avery's Career Journey
[00:02:00] Megan Dibble: I think we can maybe start off with a little bit more about your career journey. I know you just mentioned a lot of different positions, but what was your journey like finding your way into the world of data, and what made you wanna be in the data analytics space?
[00:02:16] Avery Smith: Well, for me, it started when I was in college. I think everyone goes through this journey of like, what the heck do I study? What do I choose? And I ended up selecting chemical engineering because I was like, I kind of like chemistry. I kind of like math, but not even after my first semester, like in the first semester, I was like, oh crap. I kind of hate this. How do I do something different? And specifically, I had this chemical lab technician job, so I was working in the lab outside of school. I wore a white coat. I wore goggles, I wore gloves, and I was working with these super dangerous chemicals, and I was like, man, I do not like doing this at all. How do I get out of this?
And I was kind of like already interested in programming and automation, and I, like I said, I kinda liked math, and so I stumbled upon data analytics, and it just so happened that this company that I was a lab technician for had one data scientist. So, uh, we were a small company, like 12 to 15 people, and we had a data scientist employed on staff. And I was mesmerized by his job. He got like this huge office. Everyone listened to him. Like he had so much power in the company, and he was just like chilling on his computer all day, and I was working my butt off in the lab, and I was like, get me to that. That's the job I want. And so that's kinda what sparked the interest.
Um, and then I can go through the whole journey of basically he ended up leaving the company. We tried to hire another data person for like six months. Couldn't really find anyone that was good that we could afford. And basically I was hatching this master plot. My boss didn't know about at the time where it's like, I'm gonna take his job. I'm gonna figure it out. So I like checked out every book in the library I could find. 'Cause this is like in what? This is like 2015, so this is like a decade ago now. There's not as many resources back then as there is now. So I was literally in the archaic library looking at books, trying to figure out this whole data thing, and I ended up building up this reporting tool that kind of did, I don't know, a third of what the data scientist did.
I secretly built it and I presented it to my boss, and I was like, Hey, look what I built. And he was basically like, ah, this is pretty good. You can start spending, you know, half your time doing some analysis and half your time in the lab. And then eventually that went to full-time doing data, becoming a data analyst. And then my career just leapt into that point where I was like, I'm in data now. Once you get that first job, everything's so much easier.
[00:04:37] Megan Dibble: Totally. Oh, I love that story. Definitely an experience that many listeners can probably relate to—being stuck in a job where you're just like, how do I get out of it? Looking over at people doing data, having more of that desk job, that seems appealing. Glad that you made your way into it. And
[00:04:55] The SPN Method for Landing Data Jobs
[00:04:55] Megan Dibble: now that you're doing a lot of content around helping other people switch into data, I'd love to hear a little bit more about this SPN method that you've come up with, that I've seen you produce content on. I'd love to hear a little bit more about that and how that's useful for people switching into data.
[00:05:12] Avery Smith: It's such an interesting market that we're in today, Megan. It's like, uh, there's just a lot going on, and it seems like every day there's new, crazy things happening, and the job market seems to be kind of all over the place. Some days it seems stable. Other days maybe not so much, and obviously I think because of how cool data jobs are—they're fun, they're flexible, they're frequently sought after, they're rewarding, they pay really well, you can have the chance to like work from home and stuff like that—a lot of people are trying to get into data analytics, and so it's probably harder than ever right now to land a data job.
And what I've kind of realized is there's like no direct correlation between how skilled you are and how much you get paid or how quickly you land a job. And the way that I can prove this correlation, or the lack thereof, is if I'm in my office—I work in an outdoor shed, by the way, that this is my office, is like an outdoor shed—if I just sat in my outdoor shed, and let's say I studied SQL every second of every day for a whole year, I'd be pretty good at SQL. But if I'd never applied for a job, I'm not gonna get a job. If I never told anyone about it, no one would ever know. And if, even if I'm not good at SQL, like I still have to, I still have to get past the, at s—It's not like you reach a point in SQL and you magically get paid half a million dollars a year. There's a lot of other things going on.
And so skills are just one part of landing a data job, and in my opinion, it's actually only a third. And so that's the S part of the SPN method. The S stands for skills, and then the P stands for projects or portfolio, and then the N stands for networking, and you need all three of those to land a data job. You need skills, of course, like you can't land a data job without skills, but if you're just doing skills alone, it's probably gonna lead to a lot of frustration because you're, if you're not able to demonstrate those skills in like a meaningful way by creating a project—the P part—and putting on your portfolio—the other P part—how is anyone gonna trust you, right? Because anyone can say that they know SQL well on their resume. Anyone can say that they know Python on their resume, but can you actually back that up with some sort of hardcore evidence?
And if you can, you still need the N, the networking, because really 66% of jobs are filled by a recruiter or a referral, and you're like 12 times more likely to actually land an interview if you can have some sort of network weigh in. So the combination of those three—the skills, the projects, and the networking—create the SPN method, and in my opinion, that's the easiest and the fastest way to land a data job.
[00:07:43] Megan Dibble: Yeah, I really like that combination of all three of those
[00:07:45] Essential Skills for Data Careers
[00:07:45] Megan Dibble: things. I think sometimes maybe we get hung up on the skills part because that maybe feels like the biggest gap to bridge, but spending a lot of time on the other two parts is just as important. But for the skills part, do you have advice on what is a good software to learn or what skills to build up for people moving into data?
[00:08:10] Avery Smith: I do. I'm really opinionated about this, and just know that like, so many of you guys are in different places, like there's no way that I could give you just one piece of advice that fits everyone, so everyone's in a unique situation. But that being said, for the majority of people, after going through my own data journey and then helping literally hundreds of people go through their journey, this is kind of what I found.
You basically have two different levers to play with: one, how easy a tool is to learn or not, right? And then how sought after that tool is, like how popular or how in demand that tool is. And that's what I try to base it off. All of the things that I try to make content about, that I make episodes on the podcast about, or I make YouTube videos about, or I post on LinkedIn, I try to approach from a data-driven perspective, which is hard sometimes 'cause there's not a whole lot of data, but this is one data source that I do have: we actually do this, we scrape a bunch of web listings, job postings for data analyst jobs, and then we go through and we do natural language processing to basically try to figure out what tools are listed in the requirement section.
And what it shows basically is SQL's usually the number one tool, followed by Excel and some sort of BI tool like Tableau or Power BI, and then Python. And in terms of like ease of learning, most people are pretty familiar with Excel, right? Like they've done some sort of Excel, even if it was in school or at their current job. And so Excel's a great place to start because it's really in demand and it's quite easy to learn. So that's what I start with is Excel.
Uh, and then we move on to SQL 'cause it's most in demand, and it's—it is a coding language, so it's something that's new to a lot of people, but like, really? When you're just getting started, there's like 20 different commands that you have to know. There's about a bajillion that you could know, like it's very deep, but you can get pretty far with just like 20 commands of SQL.
And then I choose to teach Tableau because over Power BI, 'cause it's just a little bit easier to access, and if you have a Mac. And it's also about 10% more in demand. So that's where I like to start is Excel, SQL, and Tableau. And then Python is a bonus. Python's awesome. It's my favorite data tool. There is a steep learning curve, so that makes it hard to start with. So I really recommend the first three, and I came up with an acronym 'cause I was like, Hey, I'm a big acronym guy if you can tell—SPN, right? And I was like, okay, how do you remember Excel, SQL, and Tableau? And so, oh, interesting. I actually, I usually do Tableau before SQL, now that I think about it, just because Tableau's drag and drop. So anyways, my acronym is now ruined, but it's "every turtle swims." Ironically, I don't think that's even true. I think there's lots of turtles that don't swim, but "every turtle swims"—Excel, Tableau, and then SQL. And then if you wanna add Python, "every turtle swims past." That's my acronym.
[00:10:52] Megan Dibble: Nice. Love it. Easy to remember. Kind of
[00:10:55] Avery Smith: silly, kind of
[00:10:56] Megan Dibble: fun. Yeah.
Yeah.
[00:10:58] Networking Tips for Data Professionals
[00:10:58] Megan Dibble: And then for the networking part, I'd love to hear a little bit more about that because I, that can feel like such a mystery. I don't know. People just say, oh yeah, network, network, network. And I'm like, I wanna know what that really means. So do you have some examples of what that can look like?
[00:11:16] Avery Smith: Totally. It's, it is a vague, ambiguous word. It's, yeah, go out there and network, and it's like, what does that look like? And once again, I think it looks different based off of your personality, what you like to do, and your strengths. So like, in my opinion, and also it doesn't have to be cringe, it doesn't have to be awkward. It, you can do it in a really cool manner, in my opinion. The less you try, almost the better it goes. And also the more that you try to focus on people who already know you and would like to help you, the better it goes.
So that ends up being family and friends. Like you don't have to be like, Hey, can you guys hire me? That's like an awkward question maybe, right? You can bring things up in a natural way. So, um, what one thing you might do with your neighbors or your friends, just tell them what you're doing. Just when they ask you, Hey, how's it going? What you been doing recently? I don't be like, oh yeah, good, I'm just hanging in there. You can be like, yeah, life's good. It's busy. I'm trying to become a data analyst right now. I'm like, I'm taking these courses, I'm listening to the Alter Everything podcast. Like I'm listening to Data Career Podcasts. Uh, I'm doing a lot of learning, and that, just saying what you're doing can open up a lot of things. Oh, data analytics. Like why that or, oh yeah. My company actually has a bunch of data analysts. Just opening your mouth and sharing what you're actually working on and what you're interested in, I think can have a big play.
And then there's other things that maybe would put other people out of their comfort zone that I really recommend. I think posting on LinkedIn is incredibly valuable. It changed my life when I started posting on LinkedIn and providing interesting—just talking once again. Just talk about what I'm doing. But that can be scary for a lot of people. So maybe just starting with commenting on LinkedIn can be a good start.
So I don't know. Those are those three ways: like you can comment on LinkedIn, you can post on LinkedIn, and you can just tell your neighbors and your friends and family what you're doing, and those conversations can happen anywhere. I can go into some examples of some students who landed jobs maybe in unusual places that you might not think about with networking because it can happen anywhere.
[00:13:19] Megan Dibble: Yeah. Yeah. I'd love to hear one of those examples or stories you have.
[00:13:22] Avery Smith: It's actually really interesting because one of my students that went through my program, she ended up landing a job off of a conversation she had at church. So she was just at church, and I think the conversation was just like, Hey, how was your week? Haven't seen you in a week. Yeah, I only see you on Sundays type of thing. And she was like, yeah, great. I'm just a little, I'm tired. 'Cause I've been studying kinda late at nights to be a data analyst. And then this friend she was talking to goes, oh, my boyfriend's a data analyst at this company. Would you want to talk to him? Yeah, sure.
So then, uh, she talked to him. He was like, oh yeah, we actually have an opening right now. Would, do you think you wanna interview? Do you feel ready? She was like, no, but yes, I want to interview. And she ended up getting the job. So, you know, church of all places. That's actually, uh, how I ended up basically going from a lab technician to a data analyst. I went back home to my parents' church during a winter break, and there was a guy there, and he was like, Hey, what are you up to? I was like, yeah, I'm a chemical lab technician. And he was like, did you know I work at a lab, like a bio lab near you and I, no, I didn't know that. Well, do you wanna come work for us instead of your other lab job? Yeah, sure. So I was still a lab technician at their company, but that, that's what led to me to becoming a data analyst there. So I'm not saying you have to go to church, but that's a great place to just open up your mouth and talk to some people.
[00:14:38] Megan Dibble: Sure. Yeah. Any kind of community setting where it's—you talked about neighbors before, but even I was at like a friend's giving at a friend's place, and it was like a friend of a friend talking about searching for jobs, and I was like, oh, we might actually have some in that domain open. Like I can go take a look. So it really is, I think that's a great piece of advice, like just sharing what you're doing. If they ask more like what you're looking for specifically, because when you have that connection, when it's a friend of a friend or some kind of connection like that, it can be a little bit easier, and people can be just so willing to share what their company has or connect you with someone who's really open to chatting about what data analytics means.
Those are some fun stories for sure.
[00:15:24] Inspiring Career Pivot Stories
[00:15:24] Megan Dibble: I'd also love to hear about some interesting career journeys or like big career pivots that you've seen from hosting your podcast. From having guests on, like what are some takeaways you've had, themes you've seen from people pivoting into analytics?
[00:15:41] Avery Smith: Well, I'll tell you that, so I think we've done like 155 episodes of the Data Career Podcast, and I think maybe a third to about half our, our interviews—so we've done, I dunno, maybe like 70 interviews or something like that. And man, you can get into data from like nearly any field. I've seen so many different fields that you might think have nothing to do with data analytics at all, and maybe they don't. But the truth is, data is everywhere.
I've seen a lot of teachers that I've had the chance to interview on the podcast or have gone through my program that have gone from teaching kindergarten to landing their first data analyst job. I had one, Erin Sheena, who's been on my podcast. She went through my program as well, and she went from a music therapist to financial analyst. Yeah, that I didn't even know what a music therapist was. Are you familiar with that term? It was not something I had ever heard of. She like worked at a hospital, and she'd visit really sick patients and play like live music for them to try to brighten their day and help them heal basically. Yeah. Super cool job. I don't really know, but it almost seems like the complete opposite of a data financial analyst like yeah, it seems like directly orthogonal—no overlap, but I can see not at all. But actually she now works for a healthcare company, and she used to work in a hospital. She does know a little bit about hospitals, insurance, and billing. Not a ton, but she had that background.
I just did an interview. By the time that this comes out, this interview will probably be up. I just did an interview with one of our students in the program who was a pizza delivery driver and now is a data analyst at a FANG company, and she told me about how when she was studying data, she was working in this pizza delivery company, and she tried to convince her manager to let her look at the data to try to create a better model for pizza wait times. She also did delivering packages, like Amazon type stuff, and in her city there was a lot of gate codes that she'd have to text the recipients for.
And she talked to me about how she, once again, like a delivery driver, not very data-centric, but she told me about how she would try to A/B test the text messages that she would send to these recipients to try to get them to respond faster or actually respond at all. So even though there wasn't any Excel at that job, she was still trying to be data-driven in her decisions, which I think worked out really well for her in like interviews and ultimately landing this job.
[00:18:06] Megan Dibble: Totally. I love the creativity there of. I mean, you can tell that person has a lot of like, desire to, to change things and make things better. That's, yeah, to kinda
[00:18:16] Avery Smith: have—you have to have that mentality when you're at a job like that where it's, this is gonna be a hard pivot, but I think what we can learn, what I learned from that is, I don't know if there's any data job, maybe like a plumber. I don't know. I think even as a plumber—I won't say this person's name, but we, this person hasn't announced that they've got a job yet—but a carpenter, and I'm probably gonna have him on my podcast, a carpenter's landed a data job. So I think any job you have right now, if you wanna get into data analytics, you can figure it out.
[00:18:43] Megan Dibble: Yeah, yeah. And just with how technology is involved in everything we do. You mentioned the texting, the door codes, like there's some pieces of technology and some data that's being created in so many different jobs, and it's like you can start with just that little piece or just that little bit of data that's maybe created from one of the processes you have at work, even if you feel like your domain is super far away from analytics.
So that's really cool. And we've had some cool stories on our show as well. I'll definitely link some in the show notes. I think transitioning from warehouse work, I think there was an article on someone who was a yoga teacher before. Um, that one's cool. All kinds of pivots. I think that, yeah, I'm sure you and I have seen two sides of the same coin. Like I'm often talking to people who are using Alteryx as one of their tools to pivot and learn quickly with the drag and drop functionality and all that, but super similar to someone using Tableau. And that's also like an easy-to-use tool. So I think it's really encouraging to see the types of pivots people can make for sure.
[00:19:53] The Impact of AI on Data Analytics
[00:19:53] Megan Dibble: Something I wanted to ask you about was, since AI is everywhere now, lots of talking about AI on this podcast for sure. I'd love to hear how you see AI shifting the data analytics landscape, like what you've seen so far.
[00:20:09] Avery Smith: It's a good question, and just being perfectly honest, we will find out, I guess as we go. I'm looking to companies like Alteryx as an example of kind of leaders in the market and seeing what they're putting out and how that might influence different things.
But if you think about what AI is, it essentially should make our job as data analysts easier. I see it more as a tool than an actual replacement for you. It helps you work faster and more optimized. It doesn't really replace a job, I don't think. And if you think about like even, you know, if you go back to—obviously before I was born—when Excel came into play, right? I think a lot of bookkeepers or even like mathematicians were like, oh my gosh, there's this new tool out here that makes math and databases and stuff easier. Like it's going to just disrupt my job and my career. And I think at the end of the day, it doesn't remove those people. It just made how they work a little bit different.
That's really no difference than Alteryx as well, right? 'Cause Alteryx, it's a drag and drop platform that makes stuff that's always been available to people a lot easier. Oh, I can do my ETL without coding. I can do this visualization without having to do any coding. So it's just a tool that makes things easier.
But I definitely think it will impact what we do as data professionals. We will work differently, and I think it is worth keeping an eye on and just to know, I think if you ignore it, it'll be to your own peril. I don't think you have to be obsessed with it, but just keep an eye on it and just be like, okay, I understand how I can use it.
For me, for example, when I got stuck coding in the past with Python—remember I said it was hard to learn Python and it's a steep learning curve—I used to do this thing called Google, and every time I'd Google a Python error, it would go to this website that maybe some of you guys who are new in the data world have never heard of. And I would love to see their traffic 'cause it has to have tanked the last five years—called Stack Overflow. It was like a forum with a bunch of basically programming bugs. That's how I saw it. And the answer would be there. And now instead of going to Google, I go to ChatGPT, and I paste in the error, and it just tells me what's wrong. It saves me a little bit of time in reading, probably. Right. Um, but I would just try to be using things like ChatGPT and staying on top of trends—maybe not on top of trends, maybe like in the middle of trends. Keep one eye on it.
[00:22:30] Megan Dibble: Obviously, you've been with people as they go through this journey of pivoting and learning and upskilling. Have you seen ways they could use AI to help with that learning or upskilling? Is that another use case or have other methods been just more helpful?
[00:22:45] Avery Smith: I think, for example, debugging code, I think AI is amazing, right? It just helps you get the answer faster. I do think—I don't know if you're a big—I'm not even big on this, but I follow software engineering on Twitter. I think it's really interesting. They're, I think they're a little bit ahead of the data world in terms of coding. They, there's some pretty cool platforms that basically will build apps and mobile platforms and stuff for you, and they call it "vibe coding," but there's a lot of chaos going on over there anyways.
It's hard to know, like sometimes you trust AI's code and it is wrong. That happens, and that will happen in data analytics as well. It's still really important to just learn the fundamentals. So where I do think it's really helpful, I think it's really helpful for debugging. I find it really helpful, almost helping me be retrospective with anything I'm working on—this project, what are things I should consider? And then it can talk me through maybe things I hadn't thought about, and maybe, maybe half of it I might ignore. I might be like, that's dumb. I don't need to worry about that. But there are some good questions I think that are in there.
I think if you ask it certain questions, it's gonna get some right and it's gonna get some wrong. But I do really think for debugging it's quite good. Another way that my students and I are using it is, and this might hurt you, I know you are a very good writer and a good blogger, but we use it to get our rough drafts of any projects we write up. A lot of the time, like I never say just copy and paste and let it go. But I think it can be great for creating that initial first draft and maybe an outline as well. So like technical writing.
I think at the end of the day, AI is—it's almost like you put on like some gloves that give you some more powers. Like you're a basketball player and you put on shoes with springs in 'em. You still gotta jump, but it can help you get a head start. Do you agree?
[00:24:34] Megan Dibble: I think I agree for the most part. Yeah. I think that especially for like where we're at right now, that feels true to me. Like it is a tool that can help accelerate things and make tasks a little bit easier. But you can't just offload all your tasks to AI right now, is what I think.
I think that in the future, it's hard to predict. It's like I'm not a generative AI developer or anything. So it is hard to predict, but from the outside I could see us offloading more and more of our tasks over time. But I do think, for me, the sticking point is that to have these good models, you have to have good data. Yeah. So it's almost this backwards loop of what you're saying about shifting what you're doing. Like maybe there'll be more focus on the data pre-processing or your job would shift in different ways to make sure that those models can be the most accurate, if it's maybe offloading other parts of your tasks.
That's my take on it right now, but it really does feel like there's new tools every day and something to keep up with. But I like the staying in the middle of it, like we don't have to be on every tool, figuring out every single use case, but keeping one foot in there to understand like how it could make you more productive, I think is like a competitive advantage more and more if you're looking for a data job.
[00:25:56] Avery Smith: For sure.
[00:25:57] Conclusion and Podcast Links
[00:25:57] Megan Dibble: Cool. Well, yeah, that was all the questions I had for you. It's been really great to have you on our show. I'll definitely link the Data Career Podcast in our show notes for listeners. I think that there's a lot of great crossover there in terms of they would be interested in your episodes and vice versa, so another podcast for our listeners to check out.
[00:26:18] Avery Smith: Yeah. Thank you so much, Megan, for having me. Would love to have any of you guys hop over to Data Career Podcast and check it out. And Megan, you can take this out if you want to, but I do think Megan's gonna come on soon. So I don't know when this is publishing, if we're gonna publish first or if she's gonna publish first. But if you guys wanna hear a little bit more about Megan, we'll have an episode over there too.
[00:26:35] Megan Dibble: Thanks, Avery. Have a great day.
[00:26:37] Avery Smith: Yeah, you too.
[00:26:40] Megan Dibble: Thanks for listening. To learn more about Avery and listen to his podcast, head over to our show notes on alteryx.com/podcast. And if you like this episode, leave us a review. See you next time.
This episode was produced by Megan Bowers (@MeganBowers), Mike Cusic (@mikecusic), and Matt Rotundo (@AlteryxMatt). Special thanks to @andyuttley for the theme music track, and @mikecusic for our album artwork.
