Structuring Your Resume for Maximum Impact

Structure your resume to clearly showcase your skills and impact. A clean, thoughtful layout can make all the difference in getting 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 a previous newsletter, we discussed how to get past the resume screen by ensuring your application is reviewed by a human rather than being filtered out by ATS or overlooked due to vague wording. Today, we’ll go one step further: how to structure your resume to highlight your most relevant skills and experience effectively.

A well-structured resume ensures that hiring managers can quickly grasp your expertise without unnecessary details. Below are the key components you should focus on.

A strong resume isn’t just about what you write - it is about how you structure it for maximum clarity and impact.

So let’s learn how to do that.

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.

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.

1. Crafting a Strong Career Summary - Optional but Highly Effective

A career summary, placed at the top of your resume, helps frame your experience in a way that immediately aligns with the role and positions you as a strong candidate. Limit it to 2-4 lines. It includes who you are, your core expertise, and a specific achievement or career highlight.

Example of a strong summary:

"Machine learning engineer with 5+ years of experience in applied ML and scalable systems. Deep expertise in deep learning, LLMs, and optimization techniques. Led the development of a real-time recommendation system at <Company>, improving engagement by 18% across 50M+ users."

Here’s another example:
“Data Scientist with 5+ years of experience in predictive modeling and A/B testing, driving business growth through data-driven decisions. Skilled in Python, SQL, and causal inference. I built a causal framework to assess the impact of budget change from Branded to Non-Branded Search and scaled it to other marketing channels.”

Now, compare this to a weak summary:

"Data scientist with a passion for AI. Experienced in Python, ML, and data-driven decision-making. Looking for an opportunity to apply my skills and grow in a fast-paced environment."

The second summary fails because it is generic and lacks tangible impact. A hiring manager reading this will not immediately see why you stand out.

The No-fluff formula by Mandy Liu:

Here’s a No-fluff formula from Mandy Liu that I really like:

“𝘐’𝘮 𝘢 𝘩𝘢𝘳𝘥-𝘸𝘰𝘳𝘬𝘪𝘯𝘨, 𝘥𝘳𝘪𝘷𝘦𝘯 𝘪𝘯𝘥𝘪𝘷𝘪𝘥𝘶𝘢𝘭 𝘱𝘶𝘳𝘴𝘶𝘪𝘯𝘨 𝘢 𝘔𝘢𝘴𝘵𝘦𝘳’𝘴 𝘪𝘯 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦. 𝘐 𝘩𝘢𝘷𝘦 𝘢 𝘱𝘳𝘰𝘷𝘦𝘯 𝘵𝘳𝘢𝘤𝘬 𝘳𝘦𝘤𝘰𝘳𝘥 𝘰𝘧 𝘵𝘶𝘳𝘯𝘪𝘯𝘨 𝘭𝘢𝘳𝘨𝘦 𝘥𝘢𝘵𝘢𝘴𝘦𝘵𝘴 𝘪𝘯𝘵𝘰 𝘢𝘤𝘵𝘪𝘰𝘯𝘢𝘣𝘭𝘦 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘪𝘮𝘱𝘳𝘰𝘷𝘪𝘯𝘨 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘳𝘦𝘴𝘶𝘭𝘵𝘴. 𝘐 𝘪𝘯𝘵𝘦𝘳𝘯𝘦𝘥 𝘢𝘵 𝘢 𝘍𝘪𝘯𝘛𝘦𝘤𝘩 𝘤𝘰𝘮𝘱𝘢𝘯𝘺 𝘢𝘯𝘥 𝘢𝘮 𝘭𝘰𝘰𝘬𝘪𝘯𝘨 𝘧𝘰𝘳 𝘢 𝘳𝘰𝘭𝘦 𝘸𝘩𝘦𝘳𝘦 𝘐 𝘤𝘢𝘯 𝘭𝘦𝘷𝘦𝘳𝘢𝘨𝘦 𝘮𝘺 𝘴𝘬𝘪𝘭𝘭𝘴 𝘢𝘯𝘥 𝘨𝘳𝘰𝘸.”

Yawn.🥱

Why does this fall flat? It’s all vague statements with zero proof. Words like “driven” and “hard-working” says nothing without examples.

Here’s one that actually sells:

“𝘐’𝘮 𝘢 𝘥𝘢𝘵𝘢 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘴𝘵 𝘢𝘪𝘮𝘪𝘯𝘨 𝘧𝘰𝘳 𝘢 𝘳𝘰𝘭𝘦 𝘪𝘯 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴. 𝘚𝘬𝘪𝘭𝘭𝘦𝘥 𝘪𝘯 𝘚𝘘𝘓, 𝘗𝘺𝘵𝘩𝘰𝘯, 𝘥𝘢𝘵𝘢 𝘴𝘵𝘰𝘳𝘺𝘵𝘦𝘭𝘭𝘪𝘯𝘨, 𝘢𝘯𝘥 𝘴𝘵𝘢𝘬𝘦𝘩𝘰𝘭𝘥𝘦𝘳 𝘮𝘢𝘯𝘢𝘨𝘦𝘮𝘦𝘯𝘵. 𝘔𝘺 𝘣𝘪𝘨𝘨𝘦𝘴𝘵 𝘪𝘮𝘱𝘢𝘤𝘵 𝘩𝘢𝘴 𝘣𝘦𝘦𝘯 𝘩𝘦𝘭𝘱𝘪𝘯𝘨 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘻𝘦 𝘢 𝘩𝘪𝘨𝘩-𝘷𝘢𝘭𝘶𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘧𝘦𝘢𝘵𝘶𝘳𝘦 𝘣𝘺 𝘪𝘥𝘦𝘯𝘵𝘪𝘧𝘺𝘪𝘯𝘨 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘱𝘳𝘰𝘧𝘪𝘵𝘢𝘣𝘭𝘦 𝘶𝘴𝘦𝘳 𝘴𝘦𝘨𝘮𝘦𝘯𝘵 𝘧𝘳𝘰𝘮 𝘢 𝘥𝘢𝘵𝘢𝘴𝘦𝘵 𝘰𝘧 20𝘔 𝘶𝘴𝘦𝘳𝘴.”

See the difference? It’s direct, specific, and shows your skills in action.

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗻𝗼-𝗳𝗹𝘂𝗳𝗳 𝗳𝗼𝗿𝗺𝘂𝗹𝗮:

1. Who you are & what you’re looking for

2. Your core skills — the tools and techniques you excel at

3. Your impact — a quick example that shows you can drive real results

Shoutout to Mandy Liu for the above framework. Follow her on LinkedIn.

💡 Pro Tip: If you apply to roles across different domains like Marketing, Supply Chain, FinTech, create multiple themed resumes with tailored summaries.

2. Tailoring Your Resume for Each Role (Without Rewriting Everything)

A common mistake is submitting the same resume for every job without minor adjustments. However, rewriting your entire resume for each application is also inefficient. A structured approach is to use themed resumes + strategic tailoring.

Step 1: Create Themed Resumes

If your experience spans different domains, maintain 2–3 versions of your resume focusing on different strengths.

For example:

• Product Analytics Resume – Highlights A/B testing, experimentation, and causal inference.

• ML Engineering Resume – Emphasizes model deployment, infrastructure, and optimization.

• NLP-Focused Resume – Showcases experience with transformers, embeddings, and large-scale text processing.

This allows you to apply to relevant roles without starting from scratch each time.

Step 2: Tailor Based on Keywords

Before submitting an application, analyze the job description for key skills and terminology.

• Identify the top three most emphasized skills in the job post.

• Ensure they appear naturally in your skills section and experience descriptions.

• Modify your career summary (if included) to align with the role’s focus.

Here’s a ChatGPT Prompt for tailoring your resume based on keywords: “Extract all relevant skills and keywords from this JD for a Data Science role at <Company Name>.”

Sample Resume Template

To make this process easier, here’s a resume template 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.

Check out the sample resume template here.

3. Resume Red Flags from a Hiring Manager’s Perspective Feat. Siddarth R, Director of Data Science at Microsoft

Siddarth R, Director of Data Science at Microsoft, shared some resume mistakes that immediately raise red flags when hiring senior data scientists and machine learning engineers:

🔴 Vague job descriptions: If a resume lists responsibilities but lacks details on execution and impact, it’s a missed opportunity. Many advises: “Tell me exactly what you did and what changed because of your work.”

🔴 Buzzword stuffing: Some candidates list every trending ML framework or technique, even if they haven’t used them in production. Many explains: “We can tell when someone just throws in ‘LLMs’ or ‘Kubernetes’ without real-world experience.”

🔴 No evidence of ownership: Senior roles require leadership and accountability. Many notes: “I look for ownership—did they drive a project from end to end, or were they just executing someone else’s plan?”

🔴 Overloaded resumes: Too many details can dilute the impact. Many’s advice: “If your resume is more than two pages, it’s likely unfocused. Highlight what’s truly relevant.”

These insights are invaluable for those aiming for top-tier roles in the industry. If you’re revising your resume, look at it through the lens of a hiring manager—does it clearly communicate impact, ownership, and alignment with the role?

4. Final Considerations

How to check if your resume is effective:

• Reverse-engineer it: Would a hiring manager be able to describe your strengths after a 10-second scan?

• Get feedback: Ask a peer or mentor in your field to review your resume objectively.

• Test with job postings: Compare your resume against roles you are interested in and check whether it directly addresses the key requirements.

A strong resume doesn’t just list skills—it clearly communicates your expertise, technical impact, and alignment with the role.

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