I lost out on $12,000 because of these mistakes. And that's not hyperbole. I actually did the math and I will show you the math during this episode. Welcome to Grad School of Sucks. My name is Matthew Croson and I'm your host. And today I wanna talk to you a little bit about my journey to becoming a data analyst and more specifically the first time I tried and failed to get a job outside of academia. When I went on the job market the first time, the non-academic job market, job market and industry. I had no idea what I was doing and I made a variety of mistakes along the way. But I think these three mistakes stand out as the most important ones that really doomed my job search and some of the easiest ones to change to be successful when you're on the industry job market. So let's get started. Here's three mistakes that I made. Number one, I didn't have a specific focus in the kind of job that I wanted to get. I knew that I wanted to work with data or in research generally speaking and so I applied to literally every kind of job that I could find that included the word data and research in some way. The result was that I applied to at least 10, if not 15 different kinds of job titles. I even applied to be a community manager at a video game company. The spray and pray approach of sending out your application to literally everything under the sun that looks like it could possibly be a fit is a sure way to waste your time and energy and not get any job offers. The second time when I jumped on the job market and had a successful job search, I only applied to data analyst positions and a few data scientist positions that I didn't get any callbacks for. And I even decided to focus on a sector where I felt like I had a competitive advantage. That advantage for me was in healthcare because I was previously a mental health provider and so I understood the healthcare system from a provider perspective. And also in my research, I looked at a lot of health related data. So I could say that I had some kind of parallel experience in that space. And what do you know? I got lots of callbacks from healthcare companies, not as many from other kinds of companies. Number two, the second thing, the second big mistake that I made was that I didn't make a data analyst resume. I see this issue time and time again with PhDs whenever they decide they want to leave academia and go to industry. They make a one page version of their CV or worse, they simply send their academic CV out whenever they're applying for non-academic jobs. I worked with a former professor who decided they wanted to go industry a couple months ago and they said it so perfectly, your academic CV will close more doors than it opens. Recruiters do not want to see your academic CV and frankly they don't even want to see the one page summary of your academic CV that most PhDs make. They want to see a resume that looks like a data analyst resume. And I know what you're probably thinking, Matt, I don't know what a data analyst resume looks like. How am I going to figure that out? It's simple, whenever you're doing coffee chats with people who already have the kind of job that you want, say in data analytics, you simply ask them at the end when you've built a rapport with them, hey, I'm working on making a resume for my job search for a data analyst position, would you mind sharing a copy of yours so I know how to better format mine? That is a perfect way to find out exactly what data analyst resumes should look like. And even if you struggle to find people who are willing to do coffee chats with you, you can literally Google data analyst example resume and sift through all the bull crap that you find that's just clickbait and actually find one or two good examples online and use those as templates whenever you're building your own resume. And before I tell you the third and final mistake that I made on the job market that cost me 12 grand, I wanna let you know that I'm holding a free online live workshop here in a couple weeks, Tuesday, April 23rd at 7 p.m. Eastern, I am going to be holding a one hour workshop where I talk about how to become a data analyst with your academic skills and experience. I'm gonna go through my journey and how I got the position that I have now in data analytics. I'm gonna discuss other mistakes that I made along the way and how I corrected them in my second job search. And I'm gonna talk about what PhDs can be doing in their job search to maximize their chances of breaking into data analytics. If you wanna sign up for this free workshop, you can check out the description. I'll have a clickable link there where you can register for free. All right, so now the third and final thing that I did wrong on my job search that cost me a ton of money. And that third thing is that I did not learn the tools that were required for the job. I came out of academia knowing a variety of things about stats, working with data and doing research. And the programs that I used to do that kind of work were SPSS, M+ a little bit of SAS, and then a handful of other smaller tools. The vast majority of those tools are not used in industry, unfortunately. The most commonly used tools in industry are going to be RSQL or SQL and Python with SQL being the most common of the top three. Now, obviously, depending on the sector that you go into, the tools that are used can change, but across sectors, those are the most common and SQL is the one I see the most, most often. And when I went on the job market, I felt like these academic tools were all I needed because I could probably pick up anything else that I needed to. And frankly, I think that's true. I think as a PhD, someone who has essentially taught themself along with a little bit of mentorship how to conduct research, you can learn nearly any tool that's used in industry, but that's not the problem. The problem is you have to convince a recruiter of that. And recruiters are looking for people who are low risk bets. They are looking for people who are the obvious candidates for the position who don't need a ton of upskilling or training whenever they arrive and can basically put their butt in the seat and start doing the job immediately. And of course, from the recruiter position, that makes sense. They're doing what is in the best interest of the company. And so you need to position yourself as that obvious choice for the position. And to do that, you're probably going to need to learn some of these tools that you're not using in academia. And I was stubborn on that first job search and I said, you know, I've done a ton of work to build up these skills. I don't need to do any more work. Someone can just pick me up and then I'll learn along the way. And sometimes that actually does work, but that's a pretty rare chance. And usually that requires you having some kind of internal connection with that team and then them knowing enough about you to be able to trust you to actually pick up the skills and do the job whenever you start that position. So for the average PhD, I think they are going to need to focus on learning the basics of these industry tools. So what I recommend you do is I recommend you think about the sector, the kind of company that you want to work in, whether that's going to be something like marketing or something like healthcare. And then go look at job postings for those kinds of jobs and look at the tools that they are using. Then go out and find free or low cost courses on how to learn how to use those tools. There's a zillion resources online. I think things like Coursera and LinkedIn Learning are great and they're low cost, but you can also find things for free like on YouTube. And if you're a PhD, you can learn these tools. It's not about whether or not you can do it. It's just about doing the work and then being able to show that you've done the work to a recruiter. And the way you show the work is with a portfolio project. You create an example project using publicly available data that is relevant to the kind of industry you want to go into. And you do some work to show them you can do the job. You ask a question of those publicly available data that makes sense for the kinds of companies you'd want to interview for. Then you show the code that you created in order to run the analysis you wanted to do. And then you show some kind of visualization to help them understand what the outcome was. That's basically all you have to do. And you host that on either GitHub or a personal Squarespace page or some other kind of site. And those are the three mistakes that I made that cost me 12 grand. And I know you're saying, how did that cost you 12 grand? Let's think about it. The average PhD in their first industry role, assuming they're not some kind of high powered STEM person who's coming into tech, biotech, or pharma and making six figures already, the average person's probably gonna make about 70K for their first industry job, which is what I made when I first started my first industry role. And when you do the math, that comes out to about $1,500 a week. Every week that you spend unemployed, you're losing out on a potential $1,500. And because I did my job search twice and both periods, it took two months of steady work of looking at applications, looking at openings, applying, doing interviewing, doing networking. It was for sure a part-time job, if not a full-time job for those two months. And the first time I did it, the first time I went on the job market and applied to 200 jobs, I got nothing. And I had to do it again. After doing all the prep work that I just discussed here, where you focus on the specific job title you want and potentially even the specific kind of company you want to work in, then you make a resume that does not look like a one-page academic CV. It looks like a data analyst resume or whatever other kind of resume you're applying for. And then you actually learn the tools, the basics of the tools ahead of time, just enough in order to be able to do an example project to show to recruiters and hiring managers. And then you go on the job market and you'll be more successful. And that's what happened to me. And so those first two months that wound up with nothing, if we look at the average pay that I would have been making, had I gotten a job after that first two months and didn't have to do the second two months, that would have been $12,000. And that's all I have for today. I hope you learned a lot and be sure to sign up for my upcoming live workshop on how to take your PhD and become a data analyst in industry. You can find a link in the description of this episode. I will see you all next week.