121: Genius Data Advice for 20 Minutes Straight

[00:00:00] Here's some amazing data advice from 8 very successful data creators. Let's get into number 1. I'm going to be like brutally honest because I think people tend to sugarcoat this process. A lot of people on like LinkedIn or YouTube will tell you like the sugarcoated version.

[00:00:13] I'm going to tell you what I truly looked for. I was on a hiring team when I was a data analyst. I was the one who gave the technical interviews and so that was my part of the hiring team

[00:00:21] and then you're right, I became a hiring manager and so then as the hiring manager, I did the whole process and usually brought in like my boss as well for some of like the final interviews.

[00:00:31] But on the hiring team just for data analysis, we always looked for someone who had a good personality and most people will tell you, well, you know, as long as you have the right skills and you get in there and you smile, that's what you need to do.

[00:00:45] I think when you're on a team, you really do look for someone who's going to fit well with your team and so I always kind of gravitated towards people who are more outgoing. And is that 100% fair? No, I don't think so but the hiring process isn't super fair.

[00:01:01] And so the people who are more outgoing, I tended to gravitate to and so did my whole team. Our whole team was very outgoing, very social and so we didn't want to someone come in and have a very different flow to them or personality to them.

[00:01:14] And so that's just like a brutal truth. I think that piece of it is actually the flow of the team and how that people gel together is really important. The second thing we looked for is being able to articulate well their skills, abilities and their experience.

[00:01:32] And so oftentimes we'd have people come in and SQL is really important. When I was on the hiring team, SQL was the most important skill because we used it like really in depth for a lot of our processes.

[00:01:44] And so people would come in and I was like, well tell me how you've done data cleaning or tell me how you use SQL. And if people can articulate really well, like here's how I use it.

[00:01:56] They were just like, oh, well, you know, I've taken a few courses in my job. I use SQL but I don't really use it that much. And they would kind of beat around the bush and I'd be like asking really pointed questions.

[00:02:07] They couldn't articulate those questions that I would think is if you've really used SQL well, you should know how to answer those questions. Because I can tell you, even at that time, I could be like, well, here's the process that I would take to clean data.

[00:02:19] Here's how I do that in SQL. Here are, you know, here are the exact steps. That's what you need to be saying and people would beat around the bush and wouldn't want to say things. And that was always a big red flag.

[00:02:30] And then the last thing that I think we would look for is someone who is technically proficient. So I was the one conducting the interviews. We would always do some type of whiteboarding and then some type of general technical interview question.

[00:02:42] And the whiteboarding, you know, like I'm sure you know, is just someone gives you like a database or a table and is like, hey, write a query to do this. Nothing crazy. Like I would not try to trick people. Like this is straightforward stuff.

[00:02:53] And we were hiring at like the mid-level. So mid-level analysts who has SQL on their resume for three years, this should be a no-brainer. This is like super simple. Like just aggregating something with a group by. Nothing crazy. Or just a simple join. Just combine these two tables.

[00:03:06] And people would have trouble with it. And that was an immediate red flag. Like we couldn't hire them. So those three things I would say are the biggest things that we looked for and like really ranked on during those interviews.

[00:03:17] But if I'm being like completely honest, the personality thing was like 50% of it. If you have a good personality, then that like really puts you higher up. Personality is very objective. And so it's hard to describe. But just somebody who's more outgoing, very friendly.

[00:03:32] That is like kind of what we were looking for. Being able to articulate and the technical interviews is the other 50%. So those two things were still very important. But if they looked like they were very teachable, if they looked like they were like really

[00:03:46] driven and we were like, you know, they may not be where we want them today. But I was like that person will be good in like a month. We would still hire them. And we did that for one of our business analysts who we hired.

[00:03:57] Who where he kind of knew SQL. But his job wasn't as intensive in SQL for that position. So we were like hey, let's hire him because he would fit really well with our team. And we trained him like he was a mentee on my team.

[00:04:09] So I trained him in SQL and within like a month he was up and running and I didn't really have to help him that much anymore. So again, it was like that trainability piece, the attitude, how driven they were did play a big role in who we hired.

[00:04:24] I was reading a consulting report recently and they polled executives at a lot of these large companies that generate more than a million dollars in revenue and asking them what is going to be the outlook on this.

[00:04:40] And more than half of them said that AI will actually increase the amount of employees. And then like another 20% said it wasn't going to have any impact and only 30% thought it was going to basically cause a loss in jobs.

[00:04:57] And with that 50% that thought, that felt that they were going to increase their jobs, the specific jobs that they thought they were going to increase were specifically related around data. So data engineers, data scientists and data analysts were like three of the jobs that

[00:05:12] were actually going to increase by AI. If you think about Excel and its invention, so previously whenever we had spreadsheets and they were paper spreadsheets and people would have to write them out and we had big

[00:05:24] old rooms filled with these accountants in it and their whole job was just sit there with a spreadsheet and calculate these numbers by hand and trying to figure it out. And then Excel was invented and you have this computer and it was advertised at the time

[00:05:39] it's going to replace hundreds of different jobs and one person is going to be able to do it. And if you look at the jobs of accountants from that time period to now, they've done nothing but increase.

[00:05:52] So because of this powerful tool, now we can do deeper dives into the data and provide better insights. We're not doing these menial tasks, we can actually do more meaningful tasks, provide more value and increase our output. You have to focus on the SPN method.

[00:06:10] All of your data career search is going to be part of the SPN method. So to land your data job, here's what you need to do. One, you need to learn the skills, the right skills, not too much of the skills, just the right amount of the skills.

[00:06:21] So if you're trying to land your first data job, for the majority of you guys, I think you should learn three skills first. The first one is data analysis with Excel. The second one is data visualization with Tableau. And the third one is data wrangling with SQL.

[00:06:36] Those are the first three things that I would learn if I was starting my first data job. Two, build a portfolio project that showcases everything you know and that you've done with your data skills.

[00:06:46] These are the evidence that you can actually do what your resume says you can do. Because I could say, hey, I can fly 100 feet in the air. And what would you say? Prove it. If I can't prove it, then why would you ever trust me?

[00:06:58] And so projects are the cheat code to giving the hiring manager, to giving the recruiter, to giving that company trust that you actually are cool, that you actually have the skills that you've developed. If you don't have the portfolio projects, how are they supposed to trust you?

[00:07:12] You know, resumes talk, projects walk. You need to have projects so that you can show them what you're made out of. And also projects bring confidence to you. Three, build your network so that way you have opportunities that can land in your lap.

[00:07:28] Instead of you applying to jobs, jobs can start applying to you. You need to be building and growing your network. And people discount this a ton, but I'm telling you it's actually the most important one.

[00:07:38] So I've thought about, you know, reframing the SPN method as the NPS because I actually think skills matter the least, you guys. I actually think it matters the least. I don't think I've told this story in either a long time or ever before.

[00:07:50] So it's really interesting with the drafting one. When I was using the product a ton, I really liked it. I said, okay, why don't I try to get an internship? There was before my master's in commerce degree and I saw they had an internship, but

[00:08:06] it was a normal undergrad internship. And so I reached out and I said, hey, I'm a grad student, but can I still apply for this? And I ended up getting selected and they gave me more pay and they essentially created a grad student internship for me.

[00:08:24] And to me, that is one of these if you don't ask, you don't know type of things where I was overqualified for the one, but they essentially made a role for me. I very clearly described that how I use the product, how I appreciate what they do and

[00:08:41] a lot of those types of things. And because I had that story piece, they were able to build something that essentially gave me a job for the summer and gave me more money than the other undergrad intern that they brought on.

[00:08:58] So that to me is a lesson in not necessarily resourcefulness, but curiosity in terms of just asking what's available because we all know that there's internal postings. We all know that there's asymmetric information where maybe they posted things on one job

[00:09:17] platform, but they forgot to post it on LinkedIn or whatever it might be. There's this idea that, oh, I didn't get this job that I really wanted and I applied for directly.

[00:09:29] But if you have a contact with a hiring manager, you can be like, oh, are there any other roles you think I might be a good fit for or anything along those lines?

[00:09:36] So we look at this job search very linearly that, oh, I have these three options and I have to go through this door. But there's probably hidden doors that if you just ask about, they almost definitely exist.

[00:09:50] And this is going to sound weird and it actually really applies to anything, not just statistics, but learn how you learn. When I took statistics, they just put a bunch of equations on the board or the wall or the,

[00:10:03] you know, and they just said, this is the equation for variance. This is the equation for the mean. This is the equation for this thing called a t-test. And there was just a bunch of equations and none of it made any sense at all to me.

[00:10:14] Now, does that mean I'm never going to be a good statistician? Does that mean I should just pick another career or class or something like that? And the answer is no, because it just meant that looking at a bunch of equations isn't the way I learn.

[00:10:30] I learn, I'm a real visual learner and I did not learn statistics during that class at all. And I got a really bad grade. But five years later, I mean, I knew I had to learn statistics for my job and I knew

[00:10:43] I knew I had to figure it out. So I just started trying to use pictures and I drew pictures about what was going on and I tried to illustrate what the statistic, what the equation was doing. And when I did that, I got it.

[00:11:01] And as a result, I became quite an accomplished statistician and one that's, you know, I mean, I feel like I'm bragging, but I was, you know, highly respected. People came to me for all kinds of problems because what they discovered is I didn't just know the equations.

[00:11:22] I actually knew what was happening. I knew what was really going on and I'd learned it in an incredibly deep way. And it was all just because I found another way to learn. So I think for any topic, but especially statistics, I personally had struggled with that one.

[00:11:45] Because to me, it's a weird topic. It's unlike, it's unlike anything you're taught in grade school, for example. Like when you take math in grade school, you know, your high school calculus or whatever, all those things, there's one answer. And that answer is like just one number.

[00:12:10] And so our whole lives up to a certain point is all about solving for single individual numbers. And statistics is using those same mathematical techniques, addition, multiplication, not that complicated stuff. But the results are weird in that it's not just a number. It's a mean plus variation.

[00:12:34] What does that mean? And why can't I just use the mean all the time? Why isn't that good enough? Because it used to be I could, you know, like when you were in high school, they're like

[00:12:45] calculate the average and you calculate the average and you get a number. That was good enough then. Why isn't it good enough now? So I had a lot of conceptual difficulty wrapping my brain around the fact that I was using

[00:12:57] math but the old things that I was doing weren't good enough all of a sudden. And I didn't understand why and I didn't understand anything that was going on because it was just, it was all things I'd done before but in a way that made no sense.

[00:13:12] And so it was very hard for me. So I'm just saying, like for statistics was when it was a profound thing where I had to find a new way to learn that wasn't just by looking at equations and doing whatever we

[00:13:25] did in the class because that didn't work for me. And so I think that's what, it's important for anyone. It was super important for me. So when you're starting out statistics and you're like I hate this, I don't get it, it

[00:13:36] doesn't make any sense, maybe it's because you're learning the wrong way or you're being taught the wrong way. And it's not the teacher's fault, everyone is different. Some people are going to respond positively to the way that teacher's teaching.

[00:13:50] So it's kind of up to you or it's up to me to find a way to make it work on my own. I mean, and one thing that's nice is there are lots of YouTube channels, there's mine obviously but there's lots of others that have different approaches.

[00:14:02] So you can try mine and go, you know, it doesn't work for me. And you can try someone else's and you can find someone that does resonate with you. And so it's not like, you know, I'm throwing you out and making you have to learn all

[00:14:15] this stuff on your own. There's teachers out there, you just have to find the one. It may not be the first one you find but hopefully you'll find it sooner or later. I advocate for people to post on LinkedIn as well. You're drawing attention to yourself.

[00:14:28] I think most people are not willing to do it but if I'm being honest, what I've observed is most people that do it end up finding jobs faster than those who don't. I could be totally wrong there.

[00:14:41] That's just kind of like what I observe or even people who I've had relationships with who do that. It's just like another layer, like you're promoting yourself, you're getting free feedback on your projects when you post about projects.

[00:14:54] You're developing connections which can lead to just like learning more but also perhaps getting referrals. You can attract recruiters to you and you know, you can't go into it with the expectation that like every time I post a project, I'm going to get 20 recruiters lined up in my

[00:15:10] inbox. I don't even get that but you will get some that reach out to you totally and some of those roles might be great, some not. But it's like if you're really trying to go all in to the data job hunt, it's another

[00:15:24] dimension that is going to help you so much. It's just having a LinkedIn presence of some sort and that's the networking aspect. There's multiple ways to network on LinkedIn. Some of it can be kind of just messaging people or whatever but engaging on other people's

[00:15:42] posts but also creating your own content through like posting projects or insights, whatever. It helps a lot of people. I think it's worth it and it's scary but that first time you hit post is a pretty foundational moment.

[00:15:56] Once you hit post that first time, it gets a little bit easier the second time. If you can get over that initial fear and I had it too. I was terrified the first time I hit post, especially the first time I hit post about

[00:16:08] anything data related but it gets easier and you're going to open up doors and networking opportunities if you do it. Prepare doesn't mean, that means a bunch of things. It means you need to know about the company that you're applied to.

[00:16:23] You wouldn't believe how many people I interview that have no idea what we do and it's frightening. They don't know who is interviewing them. So when we do a panel interview, they know who's going to be in that panel.

[00:16:33] They should know something about these people so that when they talk to them, maybe they can relate to them a bit better. Preparation for the interview itself. So interview questions. I mean, there's so many interview questions out there.

[00:16:44] Almost every job that you apply to, the questions, somebody put the questions out somewhere. So if you're not prepared, it's kind of your own fault but keep it conversational as well. Don't just like memorize the answers because if you sound like a robot, there's no way

[00:16:56] I'm going to hire you. I don't find that interesting. You know, show your personality, be yourself. And I guess the last thing is you want to make sure that you interview the people that you are potentially going to work for because you might love the job.

[00:17:09] You might think the company is great, but you might hate the person. You might get a really bad vibe from the person that's interviewing you. If that's the case, don't take the job. When people say, no, I don't have any questions. Like really?

[00:17:20] How is it possible that you don't have any questions? Surely you heard something during the interview that you want to ask about again. Like you said, just make something up, make it a conversation, show that you're interested. Analytics is just a modern version of the term critical thinking.

[00:17:34] It just happens to be data backed. And in a world where data is more prevalent, more accessible, you know, the analytical component is going to become more and more important for any job out in the universe.

[00:17:45] I believe that FP&A, financial planning and analytics is a wonderful career path for anybody. And the reason I think that is because if you understand the financial implications of decision making in business, then you understand the business and the business model and how a business makes money.

[00:18:08] I always tell people, the first thing you need to do when you get into a business is understand the business model. How do you make money? If you can understand that, you have a powerful perspective on everything that happens within that business.

[00:18:22] How are you paying to acquire a customer and what is the value of every customer that comes through the door? What are we doing with those customers? Are we doing anything to grow those dollars that they give us?

[00:18:32] Are we doing anything to not grow the costs that it takes to service those customers? Understanding the basics like that, I think gives you so much power and a valuable perspective over the work that you do.

[00:18:45] I'm dealing in budgets and those kinds of financial spreadsheets and helping create aggregations and guide my financial analysts and my analysts to make impactful schedules that we can work with stakeholders through and talk through the implications of the business.

[00:19:00] So it's analytic-y or whatever you called it, but there's dollars associated with it. And my feeling is that it doesn't matter if it's dollars or if it's data. It's all data. They just have different meaning, right? Whether it's a dollar or a unit, it doesn't matter.

[00:19:16] So it's all data at the end of the day. I hope you guys enjoyed all that genius advice from the best data analysts in the world. If you didn't know, all of these were taken from interviews I've done with them in the past.

[00:19:29] So if you look at the show notes down below, we'll have all of those different interviews. If you guys haven't listened to them, you have to check them out. There's so much more genius advice where this came from.

[00:19:40] If you're new here, my name is Avery Smith and I'm so glad you're listening to the Data Career Podcast. If you enjoyed this content, please hit subscribe and tell your friends. It helps the show grow and get better guests like these down the road.

[00:19:51] Thanks for listening, you guys, and I'll catch you in the next episode.