The skills section is the most ATS-scanned part of your resume: recruiters filter candidate databases by exact skill keywords. Here is what to list, per role, and how to present it.
Recruiters search their ATS like Google: "python AND sql AND tableau". If your resume lacks the exact term, you do not appear — regardless of how good you are. So use canonical names: "JavaScript" not "JS", "Machine Learning" not "ML" (or both).
Software developer: one or two languages deeply (Java/Python/C++), Git, SQL, REST APIs, Data Structures, OOPs, DBMS, plus a framework. Frontend: HTML, CSS, JavaScript, React/Angular, responsive design. Backend: Node.js/Spring/Django, databases, microservices, authentication.
Data analyst: SQL, Excel, Power BI/Tableau, Python (pandas), statistics, dashboards. Data scientist: Python, scikit-learn, machine learning, statistics, SQL, data visualization. AI/ML engineer: PyTorch/TensorFlow, NLP, LLMs, deployment (Docker), MLOps basics.
Business analyst: requirements gathering, SQL, Excel, Agile, stakeholder management, user stories. Marketing: SEO, Google Analytics, content marketing, campaign management. HR: recruitment, onboarding, HRMS, payroll. Finance: financial modeling, Excel, Tally, GST, accounting standards.
List 10–15 skills, comma-separated or grouped ("Languages: … · Tools: …"). Order by relevance to the target job, not alphabetically. Every skill must survive an interview question — padding gets exposed in the first technical round.
10–15 concrete skills. Fewer looks thin; more looks like keyword stuffing, which both recruiters and modern ATS scoring penalize.
One or two at most (e.g. "communication", "leadership") — and only ones you can evidence elsewhere on the resume. Concrete skills win filters.
No. Skill bars and star ratings are unparseable by ATS and invite skepticism. A plain list, ordered by strength, works best.
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