ATS Resume Checklist & Power Action Verbs
How Applicant Tracking Systems (ATS) evaluate candidate resumes across formatting readability, taxonomy structure, relevant skill alignment, and verifiable evidence.
What Modern ATS & Recruiter Parsers Actually Check
Single-column layout with UTF-8 text streams. Multi-column tables, textboxes, and floating shapes scramble OCR and token parsing.
Standard headings (Summary, Technical Skills, Projects, Experience, Education) that algorithms easily map to profile schemas.
Accurate naming of tools, libraries, and frameworks (e.g., PostgreSQL, DAX, PyTorch) that match target job requirements.
Action-verb bullet points with live portfolio links (GitHub, NovyPro, Streamlit) that human recruiters can immediately inspect.
Single-Column Plain-Text Template
Copy and replace marked placeholders with your genuine project metrics.
# [YOUR FULL NAME] [City, State, India] | [+91 XXXXX XXXXX] | [your.email@gmail.com] LinkedIn: linkedin.com/in/[your-handle] | GitHub: github.com/[your-handle] | Portfolio: [yourportfolio.com] ## PROFESSIONAL SUMMARY [Target Job Title] with hands-on project experience in [3 Core Tools/Languages, e.g. SQL, Power BI, Python]. Built [X completed end-to-end projects] analyzing [describe domain datasets, e.g. e-commerce logistics and loan risk]. Skilled in translating business questions into interactive dashboards, automated pipelines, and documented insights. ## CORE TECHNICAL SKILLS - Languages & Databases: SQL (PostgreSQL/MySQL: Window Functions, CTEs, Indexing), Python (Pandas, NumPy, Matplotlib) - BI & Visualization: Power BI (DAX Measures, Star Schema, Power Query ETL), Advanced Excel (Dynamic Arrays, XLOOKUP) - Core Competencies: Dimensional Data Modeling, KPI Dashboard Design, Cohort Retention Analysis, Business MIS Reporting ## FEATURED PROJECTS ### [Project 1 Title: e.g. E-Commerce Delivery Analytics Dashboard] | [Tools: e.g. Power BI, PostgreSQL, DAX] - Engineered a Star Schema dimensional model connecting [X,000+ records / X tables], improving data refresh efficiency by [X% measured before vs after query tuning]. - Formulated [X+ custom DAX measures / SQL queries] (e.g. CALCULATE, Time-Intelligence) to analyze [Key Metric 1: e.g. average turnaround time] and [Key Metric 2: e.g. regional margin variations]. - Delivered an interactive executive report with automated drill-through filters; published documentation and schema diagrams to GitHub. - Live Interactive Dashboard: [link to public portfolio/NovyPro] | GitHub Repository: [github.com/your-handle/project-repo] ### [Project 2 Title: e.g. Retail Loan Risk Classification Model] | [Tools: e.g. Python, Scikit-Learn, Streamlit] - Performed exploratory data analysis (EDA) across [X,000+ open benchmark records, e.g. Kaggle / RBI dataset] to identify correlation between [Variable A] and [Variable B]. - Implemented and evaluated baseline classification models, achieving [X% Precision / Recall / F1-Score] validated via cross-validation. - Deployed a lightweight interactive web interface on Streamlit Community Cloud enabling users to test custom parameter scenarios. - Live Web Application: [link to Streamlit/HuggingFace demo] | GitHub Repository: [github.com/your-handle/project-repo] ## WORK EXPERIENCE / APPRENTICESHIPS ### [Company / Organization Name] | [Job Title / Intern Role] | [City, India] [Month, Year] – [Month, Year / Present] - [Action Verb] [specific business task or workflow] using [Tools], resulting in [Measurable Outcome: e.g. automated daily MIS reporting, saving ~X manual hours weekly]. - Collaborated with [Stakeholder Team, e.g. Sales / Finance / Operations leads] to reconcile [Deliverable, e.g. monthly inventory discrepancies], achieving [X% verified data completeness]. - Documented standard operating procedures (SOPs) and query repositories for internal team knowledge sharing. ## EDUCATION & CERTIFICATIONS - Bachelor of [Degree, e.g. Technology / Commerce / Science] | [University / College Name] | [Graduation Year] - [Relevant Certification Name, e.g. Microsoft Certified: Power BI Data Analyst Associate (PL-300)] | [Year]
How to Genuinely Measure Your Project Results (No Fake Numbers)
State exact numbers from open data sources (e.g., "Cleaned 120,000 order rows across 4 relational tables from Kaggle/RBI Open Data").
Measure query or rendering latency before vs. after indexing/normalization (e.g., "Reduced query execution time from 2.4s to 320ms using indexed CTEs").
Calculate realistic time savings on routine tasks (e.g., "Automated weekly reconciliation that previously took ~4 manual spreadsheet hours per cycle").
Report verifiable test coverage or model evaluation scores (e.g., "Achieved 87% F1-score on test split using Scikit-Learn cross-validation").
Power Action Verbs
Replace passive words ("Worked on", "Helped with") with specific, actionable descriptors.