How to Get an Analytics Job
Welcome to How to Get an Analytics Job, the podcast where you build the foundation for your dream career in analytics. The goal of this podcast is to tackle the shortcomings of modern education in the analytics space by giving you advice from those actively practicing the art and science of working with data.
Our guests have included data professionals from Google, Amazon, Home Depot, PlayStation, Nordstrom and Truist (just to name a few) spanning roles from entry level, to senior, manager, VP and even a few CEOs. This has enabled us to keep a close feel for the pulse of the analytics space.
How to Get an Analytics Job
How to Get an Analytics Job Podcast ep. 134 | Featuring Cristina Samano-Romo, Senior Data Analyst at Amisive
134th Episode - How to Get an Analytics Job Podcast | Featuring Cristina Samano-Romo, Senior Data Analyst at Amisive
In this episode of the How to Get an Analytics Job Podcast (HTGAJ), we sit down with Cristina Samano-Romo, a Senior Data Analyst at Amisive. Cristina shares her journey into the world of data analytics, offering valuable insights into the role of data analysts in today's rapidly evolving business landscape.
Throughout the conversation, we dive deep into the tools and skills every aspiring data professional needs, including Microsoft Excel, Tableau, Power BI, SQL, AI, and more. Cristina also shares expert career advice, including strategies for excelling in job interviews, navigating career growth, and standing out in the competitive analytics field.
The How to Get an Analytics Job Podcast features discussions with top business analysts, data scientists, entrepreneurs, and business owners, focusing on the role of data in business decision-making, analytics job opportunities, and practical advice for career advancement. Whether you're just starting out in analytics or looking to elevate your career, the HTGAJ podcast is your go-to resource for actionable advice, career stories, and tips for success in data analytics.
so we were talking off air. So you graduated four years ago from here years ago. Yeah, all right, so walk us up to. What are you up to right now?
Speaker 1:So currently I work as a senior data analyst at a digital advertising company, and I've been there for about a year and a half, almost.
Speaker 2:Okay, so you're doing marketing analytics essentially.
Speaker 1:Yeah, essentially.
Speaker 2:Right, because it's interesting, because title kind of doesn't matter.
Speaker 1:No.
Speaker 2:I was actually just talking with Molly, our mutual friend about analysts has become very diluted. Analyst is kind of a catch-all term, but if you're a data analyst for a marketing company, of course you're looking at marketing data.
Speaker 1:Right.
Speaker 2:So walk me through that. What kind of data sets are you looking at? What are the metrics? How do you analyze that?
Speaker 1:Yeah, so there, um, one of the big things I work on is what we call an mm, which is a media mix model um, because we have media that we advertise on from like all sorts of platforms like social media, um to print, to direct mail, to email, um. And then what we're trying to do with like mmms is like trying to figure out how all of those interplay um and how that affects like conversions and you know all the other kpis that a company might be looking at um.
Speaker 1:So the the data is is not super messy, but obviously all data is somewhat messy and then you have to just find a way to bring it all together and put it into a model and get some results that can tell the story of of what your marketing dollars are doing for you okay, so that's interesting.
Speaker 2:So you said model. When I went like talking with you I pictured you were doing more like kind of like tableau or power bi dashboard work, that too okay, so what's the difference between the two?
Speaker 1:um. So I would say I I'm kind of in like a unique place right now in my role in, where I um I started as a contractor to do just dashboard visualizations for um, a specific tool in salesforce. It's very niche um and then I kind of sought other opportunities within that role because they would kind of say like, oh, we're working on this and I would just kind of express interest or express that I had experience in that and so other teams would kind of pull me in when they needed help, because you know, in most companies like every team gets overwhelmed at some point and so I would kind of be like a floater and get pulled into all these different projects just because I sometimes I wasn't necessarily an expert and I would just be like, yeah, I'm really good at this, even if I, even if I wasn't, because I know I'm a fast learner and I wanted the experience.
Speaker 2:That's great. So it sounds like over the last four years since you've been working, you've gained quite a bit of confidence. Like you, you feel like you can go and learn a new like I mean, are you? What are you learning mostly, is it new kind of business use cases or tools or some type of combination of the two?
Speaker 1:I think definitely like learning a lot of technical skills along the way, a lot of like with for, like, data visualization, um, I think a lot of it like, yeah, sure, you can take um like courses on it and do all these things, but until you're actually visualizing real data you don't really know and you're presenting it to people you don't really know, like how people are going to digest it and so you're like it to people you don't really know, like how people are going to digest it.
Speaker 2:And so you're like actually doing something that is impactful. Yeah, having a use case makes learning well. I feel like what it does is it just clarifies the path of like okay, here's the output that I need, I'm getting paid for this. If I don't deliver on this and that happens, you know, maybe on a repeating process I'm going to lose my job. So there's kind of like a baked-in pressure, and I feel like the pressure is different because when you were in my class like I don't think, you were like oh no, I need to solve this problem or I'm not going to pass the class. It's a different dynamic, right, it's a different dynamic.
Speaker 1:I think when, like in class obviously, like I was always someone who wanted to get a good grade right, um, so it was like I did feel some sort of pressure, but yeah, it's obviously different in the workplace, like it's a different sort of pressure, um, I think in class you more, so it's like easier to just kind of rely on like more like textbook book stuff or like stuff that you learn in class. There's hand-holding. Yeah, it's like a lot there's a lot of hand-holding.
Speaker 2:Yeah, it's like a lot. There's a lot of hand-holding.
Speaker 1:There's like a rubric for things versus, like when you're actually in the role, like you would think you would get more direction, but you really don't. It's kind of like here's the data.
Speaker 2:Right. Well and that I think what we're kind of starting to circle around as a topic was the concept of critical thinking. Like all across education they say critical thinking, critical thinking, but it's hard, it's really hard to incorporate that into a class because, like, if you have a textbook and then you have everything kind of like spoon fed into a structure, that's not critical thinking. That's right, that's just being kind of obedient yeah, like following directions or detail-oriented yeah yeah yeah, I think there's.
Speaker 1:There's a difference, like I know, like when doing like the minor here within business analytics, um, we did a lot of like stuff with real data versus just like maybe in other classes we would use literally like it would. The data would come from like the digital textbook, and you could download the file and then do something with it and it just it doesn't translate that well right when you're actually doing work in the field.
Speaker 2:So one of the things that you said earlier on the conversation I thought was really interesting and I wanted to circle back around, was so, for the current role that you're in you, you got hired as a consultant or contractor and then you started working on projects that were slightly outside of the scope of that. Then they eventually reached out to you and said hey, we want to bring you on full time.
Speaker 1:Right.
Speaker 2:And I think what you've done is you've built up a lot of like I don't even know what to call it like business capital or knowledge of what's going on within their business. So if they're trying to hire a full-time role, you've worked for them for a year and you've touched multiple aspects of their business you're going to start out six months ahead of somebody applying cold from LinkedIn or Indeed. So I think that was a really really. Did you do that intentionally or were you just like? I'm curious about this. I want you know I'm bored at work. I want to. I want to work on something else, or here's an opportunity.
Speaker 1:Yeah, I think I just I saw the opportunity. Somebody had reached out to me via LinkedIn and it sounded interesting and it was paying more than what I was making at my previous role and that, honestly, was one of the biggest motivators for me. And I was hesitant because I did like my previous role, but also I was like, okay, I'm super early into my career.
Speaker 1:It's not going to hurt me and also I wasn't the ability to contract, because obviously when you're contracting you don't have those like full time benefits. So I was fortunate enough to be in a position where I could take a contract role because I my benefits were under like my husband's, so like that also kind of played into it as well.
Speaker 1:Yeah, so you're like a pretty stable yeah, because I understand not everybody can just like jump from a full timetime to a contract, not knowing if they're going to get hired full-time. But doing that and like kind of just taking that leap of like, I guess, like faith, it played out well in the end.
Speaker 2:Well, it sounds like the contracting role was a big opportunity but you really maximized it because not only did you do that core job function but you were starting to do things kind of slightly outside of what was going on. I guess the word that's kind of popping into my mind, which I'm totally making up, is like you built up quite a bit of career equity within that year of contracting.
Speaker 2:I would say so yeah, because the projects that you're working on now are they. Are they starting to span, like, multiple aspects of the business or For sure.
Speaker 1:Yeah, like especially, um, like the MMM work. It's like slowly trying to expand into other clients that we have like kind of started with a few clients and now it's like broadening to other clients, um to like pick up more business, and things like that.
Speaker 2:That's awesome. Okay, so this is a funny topic for me. I think you're probably more technically savvy than I am. Like I'm, I'm 35, you're 25. So I got 10 years on you 26.
Speaker 1:Okay.
Speaker 2:So I got nine years on you, but I feel like you know more about technology and like the like. Aren't you coding in Python and stuff like that?
Speaker 1:Yeah, I do a little bit of Python coding. Yeah, I would say like I've had to pick up a lot more technical skills, like in the role that I am now, which is also something that I wanted, because I've always been interested in getting into those more like technical roles.
Speaker 2:So could you say some more about that, like what was the first technical thing you ever worked on? And then how do you go about developing those skills? You just go online and like Google or YouTube or whatever.
Speaker 1:Yeah, so I use a lot of chat GPT honestly to just kind of like trial and error stuff, I think. Like the first, I can't really recall. Like the first like super technical thing, um, maybe just like for dashboards using like javascript and like html, okay, and a lot of that I haven't touched either one of those.
Speaker 1:Yeah, so I I did take like a java j, like a JavaScript class in college as part of my major, but outside of that I hadn't like touched it. And then, in the role when I was contracting, I had an opportunity to do that. Actually, one of my first assignments was can you like develop this in the dashboard? And previously, like, people were like, oh, oh, I don't think it can be done, like it probably can, but it just takes like a lot of like manual coding in the back end. Um, and I was able to figure out how to do it using like html and javascript, even though, like, I hadn't used those tools before, like in a in like a work setting okay, so that was within the salesforce dashboard within the salesforce dashboard okay, because are you working in tableau now?
Speaker 1:because salesforce acquired tableau right they did um, but I I work with datarama, which is similar to tableau. In a way it's like a different datarama. Yeah, that's what it's called well, Well, now it's called sales. It's had many names, but it it was data Rama and now it's technically Salesforce marketing intelligence.
Speaker 2:Okay, yeah, have you worked with Einstein? Is Einstein's like the AI within sites?
Speaker 1:Yeah, we I get has a feature. I don't really use that very much, um, but I mean I know it's there, but lately I've been doing more and getting slowly. I actually just was put on a different team this past week and I'll be starting on a new team on Tuesday. Um and I will be doing a lot of power BI work moving forward.
Speaker 2:Very cool. Yeah, I haven't worked on Power BI stuff in man. It's been like eight months now. Okay, I miss it.
Speaker 1:I know the first Power BI stuff I technically did was like in your class, yeah. And then I know those video tutorials of me doing stuff on it are still on YouTube somewhere.
Speaker 2:You had those big glasses. Yeah, that's so funny, yeah, okay, so let's, let's talk about the class. Let's almost do like a retrospective of four years ago. I, how did you find out about the class? I guess let's start there.
Speaker 1:I'm trying to remember. I can't remember how I found out about it, cause I was. I mean, I was a math major, yeah, um, and then I my original like trajectory was I wanted to do actuarial science right, I remember that. I wanted to be an actuary after I graduated, because I wanted to use math and it just made sense, I guess. Um, and then it was, I think, before my senior or junior year, I can't remember it had to be your junior year.
Speaker 1:It was junior yeah, it was before my junior year, I think. Um it just somebody announced it about the analytics minor and I just had um like space in my like schedule, like enough courses, um, because I was almost done with my math major, like I technically could have graduated, I think, a year or a semester early, uh, but I chose to just do the analytics minor, just to see you know what it was about right, well, I mean actuarial and analytics.
Speaker 1:Sounds like somewhat related or similar yeah, I guess I never really got into the actuarial stuff and enough to really know what it was about. But I do know they use like excel a lot so okay.
Speaker 2:So then you showed up in my class and you're like who is this guy?
Speaker 1:yeah, pretty much, because it was like it was your class and then it was um, I can't remember what the name of the other classes were so it's case studies and business analytics and then the capstone course Actually.
Speaker 2:So I was consulting at the time and I believe you got to work with Oliver Sales and Marketing, which was a client of mine that I had for six years, and yeah, actually I mean I think we can talk about that project a little bit. I mean, don't get into the. I was going to say, don't get into the specifics. It was four years ago, you don't remember.
Speaker 2:You don't remember the specifics, but yeah. So what was the? I guess let's start with the case studies class. So what did you kind of get out of out of that?
Speaker 1:I would say I got a lot out of it in terms of like just skills that I could present in an interview to, like you know, present myself as like somebody who could do a job.
Speaker 2:Because I believe even back then, because I've changed, I've kind of iterated a little bit, but I don't think I gave you a final. I think your final was a portfolio right, right, that's correct. So it's like you can literally say here's the work that I've done and yeah, I think that really helps you kind of stand out in the marketplace.
Speaker 1:Yeah, just having like showing that you can do stuff with the data. Yeah, because prior to that, prior to those, to the analytics minor, like obviously, like I learned a lot in my math courses, stuff that I use today, like you know, just the math.
Speaker 2:A math degree is like a degree in problem solving right.
Speaker 1:Yeah, I mean essentially yeah, I would say so. Yeah, it develops those problem solving skills, but also it can be very just like academia, you know.
Speaker 2:Yeah.
Speaker 1:Like when you show up to an employer and you have a math degree and you haven't done like any um so it's like theoretical versus practical, yeah theoretical is practical, like obviously it's like. Yeah you, I understand all these like mathematical concepts and all these things, but I think it wasn't until the analytics minor where I had something where I could go into like a data analyst job interview and show them like I can right do something that will be of value to the company you know.
Speaker 2:Right. I mean, it's kind of like the, it's almost like the star methodology. It gives you like a very concrete situation, like a task, a problem to solve, an action you took, and then the result you know, or the recommendation Giving that framework, I think is is really, really. Yeah, I mean getting a math degree and an a minor in analytics. I feel like that's a great combination right there. Yeah, I think it.
Speaker 1:I think it served me well so far. Yeah, I think I, without it I'm not really sure if I would have been able to to really land like my first job. Honestly, just because, like it kind of was, the interview was kind of like heavily on like oh, what projects have you done?
Speaker 1:because obviously they knew I was straight out of college right yeah, right, and so I didn't have any job experience, um, and so being able to talk about like, oh, I worked on the data for this company, it was like who's the real company? Right there's a real data set, gave them real actionable results and got real feedback from someone was like something that I think made me stand out, probably from a lot of other people coming out with a college degree oh yeah, especially like entry-level people.
Speaker 2:You know, um, because I we actually talked about this in class yesterday of going and applying for like I have a systematic way that I look at applying and I have to kind of remember and put myself back into that situation of like you're coming fresh out of school, you don't have it. You might have like a part-time job and you might have a few projects under your belt, but it's not like I'm eight years into my career at this point so I can apply to a wide range of jobs. Versus looking for those entry-level jobs, it just it kind of thins the herd quite a bit. So, yeah, any type of like leg up you can get I think is very valuable.
Speaker 1:Yeah, for sure.
Speaker 2:So it is kind of cool that you, even while you're on campus here, you got your first exposure to marketing.
Speaker 1:Right.
Speaker 2:So I mean, I know this was four years ago, but, like, what do you remember of that experience?
Speaker 1:I remember it being like a teamwork thing, yeah, which I wasn't necessarily used to in my other classes.
Speaker 2:I bet math is like pretty solitary. Right, it's very solitary.
Speaker 1:Yeah, I guess, like the data and the output and then kind of like just that dynamic of spreading the work and just working in a team really was like the thing that sticks out to me, the most.
Speaker 2:That's interesting because I didn't think that would be the big benefit. I thought the big benefit would be like you got to work with the president of a pretty large company. Yeah, that too of course, and it's like you're solving a problem that hasn't been solved before, like they're launching a new, like sector of their business. Yeah, and they don't. There's no like, it's not. Like you know, two plus two equals, it's not. I know that's just super kindergarten math.
Speaker 1:I know, but I get what you're saying.
Speaker 2:But there's no like right conclusion.
Speaker 1:Right.
Speaker 2:And I think that's what is really challenging to teach a lot is the complexities and the assumptions you have to make because, like you're never going to have a perfect data set Right, like you have to make because, like you're never going to have a perfect data set right. Like you know, right now I'm working in the grocery business. I mean, think about all of the pressures, like you know.
Speaker 2:I mean we had that big um storm that came through, like one of our stores out in nashville is completely wiped out. So like all the sales from that, um, but then also too, like there was influence, uh, within the chicken. So like the egg sales are different, like there's so many different things and that's a skill set to itself of being able to kind of cut through all the noise and say, you know, to the best of my ability or it could be, I am 95% sure this is the right answer.
Speaker 1:Right.
Speaker 2:Because I mean, within marketing the world that you're doing now, like how, how do you deal with ambiguity or making assumptions?
Speaker 1:yeah, I mean there's obviously like a lot of outside variables that affect, like any buyer's decision, like you said, in the grocery business, like right, yeah literally never know what's gonna happen.
Speaker 1:Um, and yeah, I think in marketing it's it's the same and I think we saw that in the capstone. I think I remember, like all of us having a lot of questions about the data. I do remember that, yeah, because we were all like obviously unfamiliar with the business as opposed to, maybe, somebody who would be working there and like maybe more familiar with it. We were just like students with it. We were just like students, um, but yeah, like I think in in like the world of marketing now it's like the way we deal with it is kind of like using a lot of past data, um, right to kind of just try to predict, in a sense, and also to just understand the data that we see that comes in like on a day-to-day or like a weekly or monthly basis, um, but I mean, you really just can't predict a lot of things. Like it is very unpredictable in every sector.
Speaker 2:Yeah, I mean, it's just something that I don't think it's going to go away. In your, in your career, You're going to have to deal with ambiguity and having a class that kind of introduces you to that. And you know, because you could come reach out to me and like I could kind of help you. At least I'm not very I don't think I'm very prescriptive, I'm not like this is the right answer right, because there is no right answer right, yeah, you're, you're just.
Speaker 2:You're trying to do the best you can with with whatever assets or whatever you know resources you have. Yeah, at your disposal.
Speaker 1:That can be hard, I think, as a student, because you know resources you have at your disposal. That can be hard, I think, as a student, because when you're a student you're kind of most of your academic life is kind of like okay, here's like the right answer, or like this is what a good paper is if you need all of these things or check all these boxes versus working with, like actual company data. And yeah, I think it was a good, a good thing to learn.
Speaker 2:Do you want to talk about the like transitioning from a student to getting your first job?
Speaker 1:Okay.
Speaker 2:Cause I remember you were like quite stressed about that yeah, I mean, we don't have to talk about that if you don't want to no, it's okay, okay, all right. So let's talk about the transition from being a full-time to working, because that was, I mean, that was a pretty big challenge, right?
Speaker 1:Yeah, looking back at it, I think I was definitely more stressed out than I needed to be, but also I'm somebody who just like stresses about everything somebody who just like stresses about everything.
Speaker 2:Well, I think you're used to like having things like doing exceedingly well and having things like kind of go well for you, right?
Speaker 1:I think there's yeah, like a lot of things have been like if you do this, this will happen right, like if you do well in high school and you get good grades and you'll get into the college you want, or like be able to, you know, do x or y things, um, and then going out and like try to find a job.
Speaker 2:It's like you're not guaranteed to find the role that you want or the salary that you want right, because I I think, if I'm remembering correctly, you applied for like six weeks and didn't hear anything back yeah and then all of a sudden, it it. You got multiple interviews in one week, right, right?
Speaker 1:yeah, so it was strange I would. I would remember I would apply to a bunch of jobs and either not hear anything back or just get like a rejection Right.
Speaker 2:Which both of those are demoralizing. Yeah, they are.
Speaker 1:It's like very. It's hard to constantly, you know, feel like rejected. And then I remember finally like landing a couple interviews and then some of them them like they wouldn't disclose the pay right away and sometimes the pay would just seem really low. Even as somebody like entry level. I would kind of think like, oh, that's like really low you know, yeah, or somebody with what I felt were, you know, pretty valuable skills.
Speaker 2:Yeah Well, I mean I don't want to come off as like elitist or anything, but like you were coming into a market as a skilled laborer. You know it's not. It's not like you were a replaceable cog, like there. You know there there are specific skills that you have that not a lot of, you know it's not a very high percentage of the population has that.
Speaker 1:Yeah, I think now it's a little more saturated especially, but I think back then, like four years ago or three years ago now.
Speaker 2:That's true. I feel like you know, I've been teaching for the past four or five years, so I've kind of been following the market trends and it was interesting because, yeah, it was a really good market when you came out and then it was good for another year or two and then, I would say, about a year or two ago, the market just fell out. There were a ton of people trying to get in and there just were not very many entry-level jobs. That being said, the time that you you got in now it's like these mid to senior level roles are just wide open. Yeah, so it's like it's really congested for entry level. But then you go, you go up and also too, like you've got assets now, like you've got experience working. You know what was? What was that first role you got? Was it marketing?
Speaker 2:it was also marketing yeah, so like you, you have a pretty deep understanding of marketing at this point, like you've been working in it for years, so it's not like I mean that that's.
Speaker 1:That is very, very valuable yeah, I think, yeah, I think, when you are in the same like space, um, you gain a lot of like that business knowledge that most people just don't have. Because if there's no real way to get that knowledge unless you're actually working in the space, like I think, you could even have like a marketing degree and not actually know what's going on at a digital media agency well, it's the theoretical knowledge versus the practical knowledge, right?
Speaker 2:yeah, and it's funny because you were talking I don't know why this just like kind of sprung into my head, but you were talking about, like, understanding business. There's also another component of like what you did in the current role when you were going and getting those other projects. There's organizational knowledge too, so you know the people. You've built some type of relationship. There's some trust, trust established, so so what, essentially what you've done in in the role that you're, you've you're in now is you've built a lot of trust. Have you ever kind of thought about that? There's value in that right.
Speaker 1:Yeah, and I'm realizing that, I think very recently, I've realized that, as I've been working through different teams within my organization, as I've been working through different teams within my organization, I think, working with different project managers, different heads of teams, I've gotten to know my work and have gotten to know just, I guess, who I am as a colleague, that I am very reliable and that I do deliver good work am very reliable and that I do deliver um good work, and so I think word of that gets around, um, and I think that just being somebody who is willing to take on different types of projects that are maybe outside of my like, very specified role, um like puts me in that situation yeah, and also too is the term that's coming to my head is like stranger danger, like you're a known entity to them, so like there's, there's a track record of like okay, well, christina's done good work, so it's accurate, it's done on time, but she's also pretty flexible.
Speaker 2:She's like open to um, you know, stretching a little bit. She's also a hard worker, you know. Like like that personal relationship is is really valuable and I feel like a lot of people, especially nowadays, with like indeed, and linkedin, and I mean there's almost this like tinder application of the job market, like I applied to 196 jobs in two weeks when I was looking back eight months ago.
Speaker 1:Oh, wow, okay.
Speaker 2:Because, like, I can go on and it takes five seconds to do a one-click LinkedIn apply. I mean, it's essentially like swiping left or right on a dating profile yeah, but there's so much noise that happens within that. It's funny because Tinder came out after I was an undergrad but it seems like people are so frustrated with, like you said, you've got a friend that hates the apps. Now that's single and it's just a mess. That same kind of effect is happening on the job market. So, yeah, I just thought that was kind of cool that most people don't really think about that, like the equity that you're building within these relationships.
Speaker 1:Yeah, I think before I did also consider like that whole like job hopping aspect of things, yeah, but also I think when that was becoming trendy it was a very different job market, like right now people can't really afford to go into, to like just quit their job and go into another one, because, like you realistically never know if that role is going to be like long-term, like they could literally cut you whenever if they don't think you're a good fit.
Speaker 1:Like if you go into a new role and they're like yeah, actually they're really not a good fit, like they could just fire you if they wanted to, versus like if you're, I mean obviously in my role.
Speaker 2:They could fire me whenever they wanted to as well, but I think there's obviously more of a risk in job hopping. Um, yeah, but to push back on that, like them, they could fire you whenever they want. But if you think about it, you've worked at the company what?
Speaker 2:for two years now a year and a half that they would be losing all of the knowledge that you've gained specifically within their business and how it plugs into the broader market. You know what are the problems that they're facing and how you can proactively solve them. That like intuition that you've developed, Like that's valuable to them and I think that they're aware of that. So yeah, there's, but on the flip side of that, there's some people who stay in jobs too long and it's bad. So I feel like I've been hearing a lot of one side of the argument, not really kind of like struggling with the nuance of both.
Speaker 1:Yeah, I think definitely there's nuances to both, cause I mean, you can stay in a role for a while, like, obviously, if I'm in my role and then, um, I don't get like the pay increases that I feel like I should be getting, then yeah, maybe that's a sign that they're not valuing you as an employee and that you shouldn't go elsewhere, right, um. But I think if you can continue to grow and, like you know, if you have growth opportunities, grow and, like you know, if you have growth opportunities within the company, I think that's different because obviously, if you're just like stagnant and you're not learning anything new and you get kind of bored, then yeah, maybe you should maybe job hop if you want.
Speaker 2:but yeah, and it's also so like individualized right, like learn. It sounds like one of your big values is that you want to grow and you want to learn. But what if Susie Q over here just had a child, right? And has a young family Like that's its own set of challenges and learning, so being challenged there and then being challenged on the job, so it's no like one-size-fits-all solution.
Speaker 1:Yeah, it's kind of like whatever you want, like some people don't want to grow in their role or grow and they're like well, they value stability right, they just want to do what? They've been doing and they're they're cool with it, which is like that's cool right like. I guess I'm someone who can get bored easily like I like to be challenged right also I'm young, so I don't have, like all these other major responsibilities outside of, like my nine to five. Yeah, I don't have anyone to feed.
Speaker 2:You got to feed yourself.
Speaker 1:Well yeah, besides myself obviously All right.
Speaker 2:So do you have any recommendations for, let's say, someone's listening to the podcast, that's like a sophomore or junior in college right now. Do you have any like advice or reflections that you would want to pass on to them?
Speaker 1:I would say, to explore like courses that, um, maybe are not, if you have the ability to explore courses that are outside of your major, just to see like what's out there, to really try to get an idea of, like what you want to do after college. Because, like I thought, I really thought I wanted to be an actuary and now, looking back at it, I don't think that's something I could have done.
Speaker 2:Really Mm-hmm. That is kind of cool because when you were in my class you kind of got your first exposure to real data, right, and it sounds like I mean, obviously you enjoyed it to some degree, right.
Speaker 1:Yeah, I did.
Speaker 2:Very cool. What about it? Did you was the? Because I remember when I first got exposure to like real world data, it stressed me out. I was like this this is not formatted correctly, it's not accurate, there's missing data. How to clean that up? But it sounds like with with your experience of, it was like oh, this is a challenge yeah and this is new, and this is like I don't know.
Speaker 1:You're solving a problem that's not been specifically solved before yeah, I think it's just like being able to, because I think there's a lot of like creativity when it comes to like looking at data, um, that a lot of people don't necessarily think because it's like oh, it's just like analytics, like very like technical, but I think there's a lot of creativity that goes into like looking at the data and trying to, like you know, gain insights from it, because you do have to look at it from like all sorts of angles.
Speaker 1:And even just the data cleaning process itself, which is kind of like the lengthiest process most of the time. I think you could learn a lot about the data cleaning process itself, which is kind of like the lengthiest process most of the time. I think you could learn a lot about the data with just like cleaning it, um, and I don't know, I think it's just that iterative process of like okay, here's the data, let's clean it, and then let's like visualize it and see what we can gain from it. Like here are the questions that we have as a business. Just that there's like a lot of um, there's a lot of freedom to to solving like problems when it comes to data yeah, that's true and the position I'm in now.
Speaker 2:There's multiple ways that you could solve the problem, but that is actually secondary. What the main thing is. It's almost like being a lawyer in court You've got to be able to justify what you're putting out there. Because, they're not always going to say, hey, this seems off, but they could at any time. So you've kind of got to be ready to justify whatever recommendation you're making.
Speaker 1:Yeah, and I think you have to be ready also to look back at your work and be like oh, maybe I did make a mistake, Maybe I did look at something incorrectly or maybe I'm not looking at it through the proper lens.
Speaker 2:The right lens yeah.
Speaker 1:And that's happened to me a lot at work, and sometimes there's people in the organization who have experience and knowledge on a certain aspect or a certain channel, like in marketing, like direct mail or print. I don't really know much about those things.
Speaker 2:Those are like old school marketing, right.
Speaker 1:Yeah, exactly, that's like the old school marketing. And so somebody might have a lot of knowledge on that that I just simply don't, because I just don't know much about it. And so it's like if I'm presenting results and they're looking at it like oh, that that doesn't seem right or like are you, you know, can you tell me more about this result? And if I can't really say much about it, then it's like okay, maybe I have to go back and really question what the output is from this data set. And right, you know, maybe bring somebody else in to be like hey, hey, what do you think about this?
Speaker 2:So I've got a question for you, because I'm one of these Are you more of a big picture thinker or more of a detail person? Have you been asked this before?
Speaker 1:I don't think I have.
Speaker 2:Oh, that's a good question.
Speaker 1:I'm sure I feel like I'm. I'm probably more, I would say, like the small.
Speaker 2:I was gonna say my, my understanding of you, or like at least what I've seen. You seem very detail, detail oriented yeah, to where. I'm much more of a bigger like. I'm much more interested in the strategy aspect of it okay and like less the kind of inner workings of like the algorithm I'm like is this right or the model?
Speaker 1:that's being built Right right. Like you, like kind of building the model right yeah, and I get into the nitty-gritty of like what the model is doing.
Speaker 2:I would like for someone to kind of build that out and then I can use it to then drive a business decision and then I like quantifying that. That's kind of like where I like to sit on things, okay, which kind of makes sense. Now, as to why you're going much deeper into the technical stuff, because, like you, like I mean, I wonder, it seems like we're just kind of wired differently, huh.
Speaker 2:Maybe, Like well, because, like do you remember why you thought it was a good idea to get a math degree? Because from my perspective, like that seems like pulling teeth.
Speaker 1:Because I really like math.
Speaker 2:Really.
Speaker 1:Yeah, that was just my thing. I was like okay, I really like math and I'm good at it, so let me just my thing. I was like okay, I really like math and I'm good at it, so let me just do that.
Speaker 2:See, I can remember in high school having a moment where I was like I might have been trigonometry or something. I was like I don't like this. I don't know if I can go further with this.
Speaker 1:Yeah.
Speaker 2:Like I don't know, math felt overwhelming to me, especially when I was younger, but it sounds like your exposure to it.
Speaker 1:Yeah, I've always liked it.
Speaker 2:It was like more of like a curiosity-inducing for you.
Speaker 1:Yeah, I thought it was I don't know, I just thought it was cool, like I just really liked math always.
Speaker 2:Okay.
Speaker 1:Just like really nerdy.
Speaker 2:What's cool about like just the fact that it's logic or fact-based?
Speaker 1:Yeah, and I think it's just like. I mean, I really like the way that it can explain or that it I guess it like parallels, like nature in so many ways, like that's one of the most interesting things about math to me, like the. Fibonacci sequences, like you know, like the number 108 of the universe, just things like that always fascinated me and I think obviously when you're younger you can't understand the actual mathematical concepts of them. When you get into college you can begin to really understand these things.
Speaker 1:I don't know, I just thought it was really interesting and a lot of things kind of just clicked for me. You know, begin to really understand these things, and I don't know, I just thought it was really interesting and it just a lot of things kind of just clicked for me that maybe not everyone else like just gets it as easily, just because they probably don't have an interest in it. I think it takes an interest for it to be easier to learn.
Speaker 2:Yeah, Well, I mean, I think it's just interesting that, like we're both in kind of the analytics, you know, space, but we're very, two different, two very different personality types. Because I'm much more interested in like big picture, you know. I mean I used to joke like when I was running silverton analytics, like a lot of the data visualization work, I would be like this is bigger than this, so you should make this decision. It's like basically making picture books for influential adults, like if I was being a little cutesy about like saying what I, what?
Speaker 1:I do yeah.
Speaker 2:But you're like no, I want to like, build out the algorithm and get into. Ai and all this crazy stuff.
Speaker 1:Yeah.
Speaker 2:But I mean, I think just being aware of that is is valuable in that you can start to inform like, like what's next, like what, what? I mean? Are you thinking about getting to like more into, like data science?
Speaker 1:um, I think my next step, uh, that I've been considering this year, I wanted to get a master's, okay, yeah, so I'm considering a master's in ai oh wow, that's really cool I might do that.
Speaker 2:I might apply in june so tell me more about is what man. I'm gonna sound like a boomer here. Well, what is chat? Gpt? Is that ai? Yes okay, yeah, but and what is it doing exactly? I mean, I sound so dumb right now.
Speaker 1:Yeah, I mean I guess it's just. I mean, at the end of the day, it is kind of just like a chatbot, because it's being trained on what input you're giving it, but also like, obviously, when they built it it was trained on like all this data that we have.
Speaker 2:So is it like a? It's just like machine learning. It's like a series of nested ifs and statements. Is that what you're saying?
Speaker 1:Sure.
Speaker 2:Isn't that what the joke is right?
Speaker 1:now yeah, no, I mean it goes a lot deeper than that. But yeah, I mean, it's just.
Speaker 2:Very cool.
Speaker 1:Kind of like an advanced chatbot.
Speaker 2:Have you been on any of the episodes with michael galarnik? I think, so so he's studying machine learning for finance, okay, at georgia tech. So he I need to connect you with him because he would be somebody. So are you thinking about getting a master's or he's he's doing?
Speaker 2:a master's, I don't think I could do the yeah, well, because like that is, but getting a phd in a, in like a practical space, like that, I think it's a really interesting concept. Yeah, because you could teach, but then you could also go work for you know some type of trading company and build them an algorithm that's worth millions upon millions of dollars.
Speaker 1:I guess yeah, it's always an option but alright, cool.
Speaker 2:Well, christina, thank you for for joining us. This has been really fun.
Speaker 1:Thanks for having me, it's been great.
