"600 Applicants"? Apply Anyway. Here's Why.

You know that feeling when you find a perfect job posting on LinkedIn, scroll down, and see "600 people applied"?

Your brain immediately does the math: 600 applicants. 1 position. That's a 0.16% chance. Why bother?

I get it. I've been there. But today I want to tell you a story that might change how you think about those numbers.

The Job I Almost Didn't Apply For

Back when I was looking for my first data analytics role, I found a job posting that seemed perfect. There was just one problem: it had been posted weeks ago.

I knew there must have been dozens, maybe hundreds of applicants by then. Moreover, the job title was 'Business Analyst', while I was working in finance at that point (so I was applying for FP&A roles, reporting roles). However, if you read the job description — I was ticking all the boxes.

I was so desperate at that point and needed a job (I had just moved to Australia, and my bank balance was disappearing with every passing day). So I applied.

I got a call back.

Later I found out that I was the very last interview they held. Even worse—they'd already found their perfect candidate. In fact, they were so sure about this person that they were just going through the motions with the remaining interviews.

Here's what happened next: they liked me. A lot.

So much that they did something unusual—they made two job offers. One to their "perfect candidate." And one to me.

The Plot Twist Nobody Saw Coming

The perfect candidate? He rejected the offer.

Me? I accepted immediately.

I got the job that almost didn't happen because I applied to a posting that seemed too old, with too many applicants, for a role I had no experience in.

If I had talked myself out of applying, I would have missed the opportunity that launched my entire data career.

It was destiny. But what if that job with 600 applicants is your destiny?

Fast Forward: The Other Side of the Table

Years later, when I was running my data consultancy in Australia, I finally understood what really happens behind those intimidating applicant numbers.

We'd post a job and get flooded with applications. 200-300. Sometimes more. It looked overwhelming from the outside.

But here's the reality of what happened when we actually reviewed them:

Out of 100 applicants:

  • 30 required work visa sponsorship (which we couldn't afford as a small business)

  • 20 were vastly overqualified with salary expectations we couldn't meet

  • 25 didn't have the basic required skills

  • 10 had clearly sent generic applications without reading the job description (sometimes the Cover Letter was addressed to a different company, a different role, and had placeholders like [First Name], [Company Name])

  • 5 never responded when we reached out (we even had a candidate who scheduled an interview but we forgot to indicate the address of our office. And he never followed up. And obviously never showed up)

That left us with 4-5 actual candidates to interview.

And yes, we checked every single CV manually. Every. Single. One.

Because buried in those hundred applications was always someone brilliant who almost didn't apply because they thought they didn't have a chance.

Why "600 Applicants" Is a Lie

When you see "600 applicants" on a LinkedIn job posting, here's what's actually happening:

The Reality Behind the Numbers:

  • Many are applying from different countries and need sponsorship (most companies can't or won't sponsor)

  • A significant chunk are using "Easy Apply" to spam applications without reading the job description

  • Some are wildly overqualified and will demand salaries the company can't afford

  • Others are completely underqualified but applying to everything

  • People drop out of the process (accept other offers, change their minds, don't respond)

What actually reaches human eyes? Maybe 50 CVs. Maybe fewer.

What actually gets interviews? Usually 5-10 people.

What actually gets an offer? 1-2 people (sometimes more if they're hedging their bets, like they did with me).

Those 600 applicants? They're not your competition. Maybe 20 of them are.

The Timing Factor You Can't Control

Here's something else nobody tells you: timing is everything, and it's completely outside your control.

Maybe the hiring manager just came back from vacation and is finally reviewing applications.

Maybe their top candidate just rejected the offer (like mine did).

Maybe they just got budget approval to hire two people instead of one.

Maybe the person reviewing your CV is having a good day and your experience reminds them of their own career journey.

You can't predict or control any of this. But you CAN control whether you apply.

What This Means for You

Apply even if you're not 100% qualified. Job descriptions are wish lists, not requirements. If you meet 60-70% of the requirements, apply. Companies know they'll need to train people on some things.

Apply even if there are hundreds of applicants. Those numbers are deceiving. Most aren't real competition.

Apply even if the posting is old. Maybe they haven't found the right person. Maybe their first choice fell through. Maybe they just got approval to hire more people.

Apply even if you think you're overqualified or underqualified. Let THEM decide. Don't self-reject.

The worst thing that happens? You don't hear back. You're literally in the same position you'd be in if you hadn't applied.

The best thing that happens? You get a job you almost talked yourself out of pursuing.

The Real Competition Isn't Other Applicants

You know what your real competition is?

It's the voice in your head that says "don't bother."

It's the self-doubt that stops you from applying.

It's the fear of rejection that keeps you from trying.

Most people will see "600 applicants" and close the tab. They've already eliminated themselves. Don't be most people.

Practical Reality Check

I'm not saying every application will lead to an interview. Job searching is genuinely hard, and rejection is part of the process. I've been rejected countless times. And some of them upset me a lot.

But I've also gotten a job I almost didn't apply for.

Every single one of those "long shot" applications taught me something, even when I didn't get the job. And some of them—like that very first data analytics role—changed my entire career trajectory.

Your Action Plan

This week, I want you to do something that might feel uncomfortable:

Apply for at least one job you've been avoiding because it seems like a long shot.

Maybe it's a role where you only meet 70% of the requirements.

Maybe it's a posting with hundreds of applicants.

Maybe it's a dream job that feels "too good for you."

Apply anyway.

Customize your CV. Write a genuine cover letter. Put in the effort. Then hit submit and move on to the next one.

Because here's the truth: if it's meant to be, it's meant to be. But you have to actually TRY for it to be meant to be.

That perfect candidate who rejected the offer that eventually came to me? Maybe he got an even better offer somewhere else. Maybe that job was meant for me all along.

But none of it would have happened if I hadn't applied to a job posting I almost skipped.

Don't skip your shot.

Keep pushing 💪

Karina

This Week's Challenge: Find ONE job you've been avoiding applying to because it seems like a long shot. Apply today. Don't overthink it. Just do it.

Python Tip

How to create styled KPI Table

import pandas as pd
import numpy as np

# Sample data
df = pd.DataFrame({
    "country": np.random.choice(["UAE", "India", "UK", "USA", "Australia"], 100),
    "revenue": np.random.randint(1000, 10000, 100)
})

# Styled KPI Table
kpi = (df.groupby("country")["revenue"]
         .agg(total="sum", avg="mean", orders="count")
         .sort_values("total", ascending=False).round(2))

styled = (kpi.style
            .format({"total":"${:.0f}","avg":"${:.2f}"})
            .bar(subset=["total"], color="#4e79a7")
            .background_gradient(subset=["avg"], cmap="Greens")
            .set_caption("Country KPIs: bars for total, gradient for avg"))

display(styled)

Result will look like this:

Grab your freebies if you haven’t done already:

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Data Analyst & Data Scientist