Data Career Podcast: Helping You Land a Data Analyst Job FAST
Sep 15, 2026 · 45 min · 13 segments
Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Former LinkedIn exec explains why mass applying with AI is getting you nowhere. I asked him what works…
Jeremy SchifelingGuestAvery SmithHostSo I, I think, I think- Uh, for those of you who, who are listening and maybe haven't figured out how AI...
Um, I think, I think, Jeremy, what you're kind of talking about when you're saying, like, sending hundreds of resumes, is there's a bunch of, like, tools out there that'll essentially mass apply for you.
So, you know, if it took you, let's just say it took you 10 minutes to apply to a job, now you can apply to, like, let's just say 100 jobs in 10 minutes using AI.
I mean, you don't even need a fancy tool now, right? You could use Claude Cowork, you could use GPT Codex, literally say, "Here's my resume.
So, um, but, but you, you bring up a good point that it's like, what's actually the problem that we're trying to solve? And the thing that you said that was interesting was we're trying to talk to a hiring manager.
Um, and, and is that because at the end of the day, hiring is still happening by humans?
But as long as humans are still involved in the hiring and the actual doing of the tasks, it comes back to human nature.
That person is sticking their neck out and hiring when everyone else is doing layoffs because they have a massive pain point.
They have too much to get done and not enough talent on their team to do it today.
And so when they put out a job description, they are really putting out a cry for help from inside the Googleplex saying, "Hey, I need awesome data analysts so I can get my job done, so I can be successful in the world." But if you just blast them with 1,000 generic applications, that doesn't give them any signals of the fact that you're committed, dedicated, invested, all the things that we would be hungry for as the hiring manager.
It just says, "Hey, you're wasting my time, and now I'm on to the next." On the other hand, just to give you the sort of comparison, imagine that you did a little research, you understood who this person was, what their challenges were.
So I, I think, I think- Uh, for those of you who, who are listening and maybe haven't figured out how AI...
Um, I think, I think, Jeremy, what you're kind of talking about when you're saying, like, sending hundreds of resumes, is there's a bunch of, like, tools out there that'll essentially mass apply for you.
So, you know, if it took you, let's just say it took you 10 minutes to apply to a job, now you can apply to, like, let's just say 100 jobs in 10 minutes using AI.
I mean, you don't even need a fancy tool now, right? You could use Claude Cowork, you could use GPT Codex, literally say, "Here's my resume.
So, um, but, but you, you bring up a good point that it's like, what's actually the problem that we're trying to solve? And the thing that you said that was interesting was we're trying to talk to a hiring manager.
Um, and, and is that because at the end of the day, hiring is still happening by humans?
But as long as humans are still involved in the hiring and the actual doing of the tasks, it comes back to human nature.
That person is sticking their neck out and hiring when everyone else is doing layoffs because they have a massive pain point.
They have too much to get done and not enough talent on their team to do it today.
And so when they put out a job description, they are really putting out a cry for help from inside the Googleplex saying, "Hey, I need awesome data analysts so I can get my job done, so I can be successful in the world." But if you just blast them with 1,000 generic applications, that doesn't give them any signals of the fact that you're committed, dedicated, invested, all the things that we would be hungry for as the hiring manager.
It just says, "Hey, you're wasting my time, and now I'm on to the next." On the other hand, just to give you the sort of comparison, imagine that you did a little research, you understood who this person was, what their challenges were.
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