Why Getting the Job is Harder than You Think

Data Science hiring can feel like a black box. I break down the real hiring funnel data from two data science managers to reveal what happens after you apply, and what it takes to land an offer.

Mar 21, 2025

👋 Hey! This is Manisha Arora from PrepVector. Welcome to the Tech Growth Series, a newsletter that aims to bridge the gap between academic knowledge and practical aspects of data science. My goal is to simplify complicated data concepts, share my perspectives on the latest trends, and share my learnings from building and leading data teams.

“I keep applying, but I never hear back.”

“I got through the final round… but still didn’t get an offer.”

“I have a hiring manager chat coming up. I don’t know what to expect.”

If you’ve ever felt this way, you’re not alone.

Over the years, I’ve coached hundreds of Data Scientists—many of them incredibly skilled—who still struggle to navigate the hiring process.

Most candidates focus on SQL, ML, and case studies, assuming that technical prep is all it takes. But here’s the reality: hiring managers evaluate far more than just technical skills.

🚀 That’s why I’m launching the Hiring Insights Series.

The hiring process for Data Science (DS) roles can feel like a black box—long wait times, minimal feedback, and rejection emails (or worse, silence!) leave many candidates wondering: What actually happens after I apply?

With an industry-wide response rate below 15% and a job-to-applicant ratio exceeding 1:200, even highly qualified candidates often struggle to land interviews. Understanding what goes on behind the scenes can help you target the right roles, optimize your approach, and improve your chances of success.

In this blog series, we break down the hiring funnel step by step, offering transparency into what happens after you hit "Submit" on that application. The goal? To help you strategically navigate the hiring process and increase your chances of landing that offer.

About the Authors:

Manisha Arora: Manisha is a Data Science Lead at Google Ads, where she leads the Measurement & Incrementality vertical across Search, YouTube, and Shopping. She has 12+ years of experience in enabling data-driven decision-making for product growth.

Banani Mohapatra: Banani is a seasoned data science product leader with over 12 years of experience across e-commerce, payments, and real estate domains. She currently leads a data science team for Walmart’s subscription product, driving growth while supporting fraud prevention and payment optimization. She is known for translating complex data into strategic solutions that accelerate business outcomes.

Let’s Start with Some Numbers

Before diving into strategies, let’s analyze what the hiring funnel actually looks like.

Case Study #1: Banani’s Hiring Funnel at Walmart

Banani recently hired for a Data Scientist role on her team. Within just a few days of posting, she received 230 applications—many from candidates with strong educational and professional backgrounds.

Here’s how the hiring funnel-shaped up:

22% of applications passed the initial recruiter screen.
13% were shortlisted for further assessment.
7% advanced to a hiring manager screen.
2% made it to the technical interview.
<1% received a final offer.

This results in a <1% success rate.

Let’s consider another scenario:

Case Study #2: Siddarth’s Hiring Funnel at Microsoft

For another Data Scientist position, Siddarth’s team received 500 resumes.

20% of applicants moved past the initial recruiter screen.
4% (18 candidates) progressed after an additional screening round.
10 candidates reached the hiring manager interview.
2 candidates made it through the final onsite interviews.
1 offer was extended after internal discussions.

In both cases, the selection rate was below 1%.

Shameless plugs:

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So… How Do You Get Into the <1%?

Many candidates assume that strong technical skills alone will secure them a job. But while SQL, ML, and case studies are crucial, they’re not the only decision-making factors.

Other key influences include:

✅ Referrals – Having a warm introduction significantly boosts your chances of getting noticed. ✅ Confidence & Communication – How you articulate your thought process matters just as much as the solution itself.
✅ “Likeability” & Team Fit – Cultural fit and how well you connect with the interviewers play a big role.
✅ Unconscious Bias & Internal Decisions – Sometimes, decisions are influenced by factors outside your control.

Ready to Demystify DS Hiring?

This series is for data professionals at all levels who want to:

✔️ Break into Product Data Science

✔️ Understand how hiring actually works

✔️ Land better offers with confidence

Through this Hiring Insights Series, I’ll break down the hiring process from both the candidate and hiring manager perspective. We’ll cover:

The hidden hiring funnel - what happens after you apply?

How are resumes screened (and what makes yours stand out)?
What hiring managers are really looking for in each round?
How to stand out as a Product DS?
What happens in hiring panel debriefs?
Compensation negotiation tips
Why sometimes even the most technically skilled people get rejected?

Being more aware of the entire hiring process will help you play the game more strategically—and ultimately, land more offers.

Stay tuned and subscribe to the newsletter.

Upcoming Courses:

Product Data Science
Master Product Sense and AB Testing, and learn to use statistical methods to drive product growth. I focus on inculcating a problem-solving mindset, and application of data-driven strategies, including A/B Testing, ML, and Causal Inference, to drive product growth.