“Avery, I’ve applied to a ton of data jobs and haven’t gotten one interview. Is that market dead?”

To answer this, I’d like to introduce The Data Interview Formula ©️

Many aspiring data professionals think: Applications → Interviews. The more the better. But it’s more nuanced than that. I actually think there is actually 10 variables…

1. Number of Applications

2. Resume Quality

3. LinkedIn Quality

4. How Much You Network

5. Market Status

6. Location Quality

7. Job Flexibility

8. Average Number of Candidates

9. The Earlier You Apply

10. Luck

And the full equation…

(Number of Applications x (Resume x LinkedIn) x Networking^2 x Market x Location x Flexibility x Early Apply x Luck) / Number of Candidates = Number of Interviews

1. Number of Applications

Number of applications still matter. Simple math: 0 applications = 0 interviews. And yet, so many of you reading this have applied to less than 10 data jobs. Why? Imposter syndrome. Fear. Perfectionism. In this market, expect to apply to 100+ jobs unless you crush the next few variables.

2. Your Resume

Let’s say you apply to a million data jobs, but if your resume sucks, your interview count will still be zero. This is especially true when 99% of listings use an automated Applicant Tracking System (ATS). Your resume is the only thing that matters up front.

3. Your LinkedIn

Same with your LinkedIn (to a lesser extent). If you have a half-filled out LinkedIn and apply to thousands of jobs, you’re still gonna struggle to land interviews.

4. Networking

97% ignore networking, but it's the most crucial factor (hence why it's squared). People hire who they trust. Networking gets you recommended AND skips the terrible ATS systems. Skip networking? Skip interviews - simple as that.

5. Market

This is the part everyone assumes is causing them issues - and it is, to a degree. The job market is tough, but not dead. Crazy 5 years with COVID, AI, inflation, war - yet stocks are up 94%. Data job market? 5/10 health (4/10 for juniors). Don't let random YouTubers scare you. It's not 2017-2019's perfect market, but it's not zero either.

6. Location

Where you lives matters. There’s a lot of data jobs in Seattle, San Fran, NYC. Not a lot of data jobs in Wyoming, South Dakota, and Idaho. But keep in mind more candidates are in those tech hubs too: it’s a tradeoff. I’m currently working on a study highlighting the best places to land a data job in the US - coming soon.

p.s. my Accelerator students did most the number crunching - I let them intern for my company to gain experience. Interesting in joining The Accelerator and interning with me?

7. Flexibility

Being flexible with job preferences = more interviews. Want only remote healthcare analyst jobs? There are way fewer options. Only 16% of data analyst jobs are remote. Open to hybrid and in-person? More opportunities, less competition

8. Average Number of Candidates

More competition = lower chances. Recruiters are human and can't review everyone. You'd rather compete against 20 people than 2,000. This brings me to my next variable…

9. When To Apply

Apply early or your chances tank. Jobs posted 4+ weeks ago? They're probably already interviewing and not looking at new candidates, even if the posting is still up

10. Luck

Luck will always play a role in landing a job. Some people get luck. Some get unlucky. If you follow the previous tips, you can create your own luck.

If you want the formula to give you a rough estimate of the number of interviews you’ll get, please look at this free calculator I built. Okay, Claude helped me build it - but it’ll give you a somewhat realistic view of how many interviews you might be able to expect!

You need to consider all variables when trying to land a data job! I hope you find this helpful in learning why you might not be landing interviews.

If you have questions specific to your journey, just hit reply! I’ll respond.