Not getting data analyst interviews? Watch these 10 min

Over the last year, I have spoken to over 260 individual aspiring data analysts as part of the selection process for my mentorship program. And one of the biggest challenges that I see people face across the board is just struggling to get interviews and not really knowing why. The thing is, as analysts, we need to actually be analytical throughout the entire process and track our metrics. In this case, the resume to interview conversion rate, and if something is really not working for us, we need to figure out why and then use a different strategy. If you are getting a less than 7% conversion rate on your resume, and by that I mean you're sending out a hundred, uh, you're sending out a hundred applications and you're getting less than seven interviews from those applications, it means that something is not right. So in today's video, I want to talk to you about the three main reasons that I see for why people are falling into the resume wall and how we can actually overcome them. At the end, I'm going to share with you a tip and some thoughts on what has helped my students significantly expedite their job hunt process. So one of the main reasons is kind of obvious when I say it out loud, but it's just not having enough experience. If less than 30% of your resume is highly data relevant, and by highly data relevant, I mean a few different things. I mean, it uses real data analytics tools. It uses data set that is representative and realistic of the kind of data that we work with on the actual job. And the third part is actually that you've delivered insights to real people, real stakeholders. If less than 30% of your resume has that, you're probably going to get passed out for other people with more experience. And this is the case right now for even entry-level roles. That is what I'm seeing in the market across the almost 100 students that I've now mentored over the last year and a half. So the biggest question here is, okay, well, how do I get experience without experience? And I talk about this in the channel a little bit, but the main, there's a lot of creative ways to get experience outside of a full-time data analyst role. One of them is volunteering. There's a lot of volunteering data platforms where you can volunteer as a data analyst for a few months, which is great because then you actually get to get real work experience without the pressure of work while working with real data sets. The other is to do freelance projects. And even if this is unpaid, you know, you're tapping into your secondary network to find if there are people or small businesses that need help surfacing insights. The main point here is just being able to put the title data analyst on your resume. As soon as you have data analyst on your resume, your resume then takes a massive jump up in terms of the relevance to recruiters. And at that point, the question isn't so much of, well, why does this person want to work in data analytics? The question then becomes, what can this person do as a data analyst on my team? There's a few students who have done this really well. There was one student who was a therapist before transitioning into analytics. He networked his way to a data analytics internship and then eventually turned that internship into a full-time position. Then there was another student who was working in IT and he actually volunteered as a data analyst for about six months. And the main advantage there was he worked with really large data sets. And so he was able to talk about the volume and the challenges that came up from those data sets in his interviews. And he landed a data analyst job where he actually consults for Jaguar, the car company. Then I also had another student who was an undergrad. He didn't have any work experience. He had worked in a store and he volunteered as a data analyst for a few months. And then alongside with the program, because he could put data analyst on his resume, he started getting more interviews. And he also networked his way into that final opportunity that then became his full-time analyst role. So across the board, the main thing here is not just being able to put data on your resume, but also being able to build your own confidence that you can do the job through real world experience. I think a lot of times we want the job so badly that we're like, I'll just do anything, you know, not really thinking about the fact that you also want to make sure that once you get to that job, you can actually succeed. So instead of trying to take one massive jump in one go from where you are now to a full-time data analyst role, if you don't have any experience, start thinking more creatively about how you can build the rungs of that ladder to get more experience step-by-step so that when you get to the interview, you actually have experience to talk about. The second most common reason is not translating your resume and your experience in a way that actually resonates with a data hiring manager. So many industries have transferable skills. You know, anytime you've worked with project management, anytime you work with technical tools, anytime you've had to collaborate with lots of different kinds of people or communicate or present on the job, those are all skills that translate to analytics. It's not just the technical stuff. But I think a lot of times people really struggle to represent this and their story on their resume in a way that a recruiter can basically say, oh yeah, this is relevant to this specific data job. I've had students who transitioned from a variety of different industries that by title don't seem really relevant to analytics. So I know it can be done. But the main thing that I've seen transform their job hunt process is being able to do this translation on their resume. So a few recommendations here in terms of actually translating your resume. First is to just find like five to 10 job descriptions of the jobs that you are interested in applying to and really take note of the concepts and the phrases that they are using in those job descriptions. Anytime those phrases are relevant to what you've done in your previous positions, make sure that those exact phrases are included in the resume. Then you can write a draft of your resume using as many of these phrases as possible and actually use ChatGPT to help you a little bit here. So you can feed ChatGPT a draft and then you can also send it the actual job postings that you're interested in applying to and say, can you help me refine this resume as much as possible to gear this resume towards these specific data analyst roles? Of course, you should actually just take this draft from ChatGPT and still make it your own so that it doesn't get flagged as just being completely written by AI. Now, the third reason doesn't actually have to do with the resume itself. It has to do with the jobs that you're applying to. And it's just applying to jobs that are not the right fit for you. A lot of times I hear people say, I'm just praying and praying because I want to feel like I'm doing something, but that energy and time isn't being used in the best way possible if you're just applying to jobs that are way far out of reach for where you are right now. And so just be mindful of the distribution of the kind of jobs that you're applying to. What you want to aim for and what I've seen work well for students is to spend about 60% of those applications applying to jobs that are the right fit for you. And when I say the right fit, I mean, you can check off 70% of the bullets in the job description. If you cannot check off more than 70%, it just means that there's going to be other qualified candidates. And so it's actually better to use that time that you would have spent praying and praying working on that first step that I mentioned of getting more experience. So 60% of jobs that are the right fit for you, 30% of jobs where you're slightly over qualified, either in terms of years of experience or the kind of relevant experience that you've had in the past. And then the rest of that spend that applying to jobs that are slight reach for you. When you start getting interviews, you can change this distribution and play around with applying to jobs that are a little bit more of a reach for you. But the main goal right now is just to get interviews. Something that I noticed with students is the first interview is by far the most nerve wracking, right? And people tend to be really nervous. They tend to need a lot of practice for that first interview. By the time people are doing a second and third interview, they're a lot more confident and that's usually the job that they end up landing. So if you think about it, getting and failing an interview is actually part of the process. And so what you want to do is get the ball rolling as much as possible, just getting interviews, not necessarily applying to only jobs that are your dream jobs. And then just getting stuck, not getting interviews in the first place. Ultimately, there's not really a shortcut to not having relevant experience. So yes, it takes a lot more time and energy up front to focus on getting that experience. But the payoff is huge because you're going to get more interviews. You're going to do better in those interviews and you'll do better on the actual job. My last tip is to start networking. I think a lot of times when people think of networking, they're like, oh, I'm going to have to go to career fairs. I'm going to have to do all this like cringy, cold messaging. What I mean by this is number one, just putting out there that you are looking for jobs. I would say 40 percent to 50 percent of the students who land jobs really quickly in the program. And by that, I mean like less than eight weeks. It's done through networking. It's not done through cold applying. It's just the nature of how competitive this market is right now, because I mean, for good reason. Data analytics comes with a lot of perks of working in the industry. And so don't be intimidated by how high the bar is. Instead, be creative. Just start thinking more about, OK, who can I start reaching out to? Who can I start to have coffee chats with? Who can I ask if they have any connections to certain companies or data analysts just to set up informal coffee chats? Because those kinds of coffee chats, those kinds of meetings, they often very naturally turn into something like, do you mind if I just pass my resume along to you? Because I notice that there is a position that's open at your company. That's a lot more effective than sending out like 100 cold applications. So the biggest thing here to expedite your job hunt is to just start networking now, even before you think you're going to actually need it. I'm really curious about what other challenges you guys are bumping up against. If you want to leave me a comment with the other roadblocks that you're finding in terms of the job hunt process, in terms of understanding how everything fits together in the day to day job, just drop me a note below. I'm going to mark more of these kinds of just real talk videos, just sharing some of my insights and observations of what has really helped students in my program succeed. That's it for this video. I hope it was helpful. Let me know what you think below, and I'll see you in the next one.