Crafting a resume that stands out — especially in competitive fields like data science and machine learning — is more than listing tools and titles. It’s about aligning your experience with what employers are actively looking for. Over the years, I’ve developed a reliable and repeatable method to build highly customized, ATS-optimized resumes that consistently get interviews.

My approach follows a structured 7-part framework. First, I begin with a short executive summary that highlights years of experience, domain expertise, key technical strengths (like ML, GCP, Python), and soft skills such as collaboration, innovation, and analytical thinking. This acts as a crisp elevator pitch that hooks recruiters quickly.

Next, I group technical proficiencies into three focused skill categories: ML Infrastructure & Tooling (Kubernetes, TensorFlow, CI/CD), Machine Learning & Analytics (NLP, production ML, scikit-learn), and Collaboration & Delivery (Agile, product thinking, cross-functional teamwork). Each section is kept concise but keyword-rich — tailored precisely to the job description.

For the work experience section, I stick to three bullet points per role. Each bullet starts with a strong action verb, includes a measurable outcome, and integrates key role-specific language. For example: “Built anomaly detection model in BigQuery, reducing fraud-related false positives by 34%.” The goal is clarity, precision, and alignment with hiring needs.

Education and certifications follow a clean format: university name, degree, graduation year, and focus areas. I include any relevant certifications with scores (such as AWS ML Specialty – 920/1000) when they add value.

A few final rules I follow: I avoid complex templates, never use tables or graphics that confuse ATS, and steer clear of vague phrases like “motivated team player.” Every resume I build is tailored — no shortcuts. Each job description deserves its own unique response, and that’s where the real edge comes in.

This resume-building method has helped me secure interviews at major companies and has proven effective across multiple industries. In future posts, I’ll walk through real examples, resume makeovers, and breakdowns of what hiring managers are actually scanning for. If you're serious about breaking into tech or levelling up your data career, this is where it starts.