Cracking the Resume Screen
Most data science resumes get rejected before a human ever sees them. In this edition, we unpack why that happens—and how you can beat the odds with a resume that actually gets noticed.
👋 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.
In the last edition, we broke down the data science hiring funnel - how hundreds of applications get filtered down to just a handful of final interviews. We saw real hiring numbers from Hiring Managers Banani Mohapatra from Walmart and Siddarth R from Microsoft:
📌 Walmart: 230+ applications → <1% received an offer
📌 Microsoft: 500+ applications → 1 final hire
These numbers might seem disheartening, but understanding where and why candidates get filtered out is the first step to improving your odds.
So let’s start with the first (and biggest) hurdle: the resume screen.
The reality is, most rejections happen before a human even looks at your profile. If your resume doesn’t stand out in seconds, it gets passed over.
A great resume won’t guarantee a job, but a poorly written one will get you rejected before you even have the chance to interview.
Why? And what can you do to get past this filter? Let’s break it down.
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.
Karun Thankachan: Karun is a Data Scientist at Walmart, where he leads workstreams to improve item availability for Walmart E-commerce. He has 8+ years of experience in developing ML-powered solutions to improve customer experience in E-Commerce, FinTech and EdTech.
🔍The Resume Screen: What Happens After You Apply?
When you submit your application, your resume first goes through these two filters:
1️⃣ The ATS (Applicant Tracking System) Filter
Many companies use ATS software to scan and rank resumes before a recruiter sees them. The ATS is designed to:
Parse resumes for keywords from the job description
Rank resumes based on relevance
Flag missing qualifications (e.g., if "Python" isn’t explicitly mentioned, your resume may not rank high)
Why the ATS Might Reject Your Resume
Overly designed formats: ATS struggles with complex layouts, graphics, or multiple columns.
Missing relevant keywords: If the job description includes "A/B Testing" but you wrote "experiments," the system might not recognize the match.
PDF parsing issues: Some ATS systems have trouble accurately reading PDF files, so consider submitting in both PDF and plain text formats if allowed.
How to Optimize for ATS
Use a simple, clean format with standard section headings (e.g., "Experience," "Education," "Skills").
Mirror key phrases from the job description without excessive repetition.
Submit in both PDF and Word format if the system allows.
2️⃣ The Recruiter Screen
Once past the ATS, a recruiter typically spends 6-10 seconds skimming your resume. They look for:
Relevant experience: Does your background align with the job?
Clear structure: Is key information easy to find?
Quantifiable impact: Have you demonstrated business value in past roles?
A poorly structured resume, even with strong experience, can fail this stage.
📝 The Ideal Resume Structure
A well-structured resume should clearly highlight your expertise, achievements, and impact. The ideal format includes:
1. Header
Include your name, contact information, LinkedIn, and GitHub (if relevant). Avoid unnecessary details like full addresses.
2. Professional Summary (Optional)
A brief 1-2 sentence summary that highlights your expertise. For example:
Data Scientist with 5+ years of experience in predictive modeling and A/B testing, driving business growth through data-driven decisions.
Avoid generic statements like "Hardworking professional passionate about data science." Instead, focus on your impact and technical skills.
3. Experience Section
This is the most important part of your resume. Each role should include:
Company Name | Job Title | Years Worked
3-5 bullet points per role, each demonstrating impact using the Problem-Solution-Impact (PSI) framework:
Problem: What was the business or technical challenge?
Solution: What technical approach did you use? Be specific (e.g., XGBoost, Causal Inference, A/B Testing, LLMs).
Impact: What was the measurable outcome? Use business metrics where possible.
Example of a Strong Bullet Point
Developed an ML-based demand forecasting model (XGBoost) for a global supply chain company, reducing inventory costs by 15% (~$10M annually) and improving order fulfillment by 8%.
Example of a Weak Bullet Point
Built an ML model for demand forecasting using Python.
(Too vague, lacks business impact and methodology.)
If a bullet point becomes too long, break it into separate, digestible bullets:
Developed and tuned an XGBoost model, outperforming baseline by 20% in RMSE.
Engineered domain-specific features, improving model stability across seasonal shifts.
Deployed the model in production, reducing forecasting errors and saving $5M annually.
4. Skills Section
Designed for both ATS and recruiters, this section should:
List role-specific keywords (e.g., Python, SQL, A/B Testing, Causal Inference).
Avoid generic or overly broad terms (e.g., "Data Science" is too vague—use "Fraud Detection" if that is your domain expertise).
Be placed towards the end of the resume, as recruiters prioritize experience over skills lists.
5. Education Section
Include your degree, university, and graduation year. If you have significant industry experience, keep this section brief.
Sample Resume Template
To make this process easier, here are two resume templates you can use as a reference. This template is optimized for both ATS and recruiter readability, ensuring that your key skills and impact are highlighted effectively. Huge thanks to Karun Thankachan and Sai Kumar Bysani for building these resume template.
Resume sample 1 by Karun: Sample resume template here
Resume sample 2 by Sai Bysani: Sample resume template here
They are amazing data creators, so give them a follow!
Common Resume Mistakes to Avoid
Listing duties instead of achievements: Don’t just say you “built dashboards”; specify how they impacted decision-making or improved efficiency.
Using vague language: "Worked on data pipelines" is far less impactful than "Optimized Spark-based data pipeline, reducing processing time by 40%."
Lack of quantifiable impact: Whenever possible, tie your work to business outcomes (e.g., revenue impact, cost savings, efficiency improvements).
🔑 Final Thoughts: Cracking the Resume Screen
The resume screen is the biggest filter in the hiring process, eliminating over 75% of applicants before they even get a chance to interview. Most Data Science candidates focus intensely on technical interview prep but underestimate the importance of a well-structured, impact-driven resume.
A strong resume isn’t just a list of skills and job titles—it’s a marketing document that convinces recruiters and hiring managers that you’re worth interviewing.
A great resume won’t guarantee a job, but a poorly written one will get you rejected before you even have the chance to interview. Invest time in crafting a resume that tells a compelling story of your impact.
In the next edition, we’ll move to the recruiter screen and hiring manager call—what they actually look for, and how you can position yourself as a top candidate. Stay tuned!
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