Aman Gupta
Getting shortlisted for IT jobs in India is a chain — a parseable resume, exact keywords, eligibility, timing, and referrals. Here's what actually moves the needle, stage by stage.
Jun 25, 2026 · 11 min read
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Start from an ATS-tested template, or check how your current resume scores — both are free.
A data scientist with a Kaggle Expert badge, three end-to-end ML projects, and production experience in Python and TensorFlow. Applied to 28 roles. Two replies. The models were solid. The resume was a two-column template with a skills radar chart and a row of framework logos — and every ATS read it as a scrambled, half-empty page.
An ATS-friendly data science resume is single-column, plain-text, built in Word or Google Docs and exported as a PDF — with your stack (Python, SQL, scikit-learn, TensorFlow) written as text, your models tied to business outcomes, and your Kaggle and GitHub links in plain text, never as badges or graphs. Across the 1,000+ resumes ResumeLens has analysed, 70%+ of low ATS scores were caused by complicated design, not missing skills — and data science resumes are among the worst affected, because the field gravitates toward notebook-style, visually dense formats.
This guide covers the correct structure, the right keywords for data analyst vs data scientist vs ML engineer roles, the single content fix that separates DS resumes that get callbacks, and section-by-section guidance to get past the scanner.
➡ Check if your data science resume is ATS-readable right now — free scan on ResumeLens.
Data scientists face the same template trap as developers, plus a targeting problem unique to the field. The DS community gravitates toward Jupyter-style layouts: two-column templates with a dark sidebar, framework logo grids (Python, R, TensorFlow icons), skill proficiency radars, Kaggle medals, GitHub contribution graphs, and embedded charts or LaTeX equations. They look technical and impressive to a human. To an ATS parser, the columns merge, the graphics extract as nothing, and the equations become garbled symbols.
When an ATS receives a multi-column PDF it reads across the full width of the page, fusing your work experience with your skills sidebar into one unreadable block. And the most damaging mistake — the same one ResumeLens found across hundreds of fresher resumes — is submitting an image-based or notebook-exported PDF that contains zero extractable text. The file opens fine; the ATS reads nothing.
The fix is structural, not a content change. It starts with the correct format.
Put your Skills section near the top, right after your summary — recruiters and ATS both scan for your technical stack in the first few seconds. Add a Publications section only if you have peer-reviewed papers; otherwise Projects carries that weight.
| Order | Section |
|---|---|
| 1 | Contact information |
| 2 | Professional summary (titled with your role) |
| 3 | Skills |
| 4 | Work experience |
| 5 | Projects / Publications (move above Work Experience for freshers) |
| 6 | Education |
Use your actual role as the heading — not "Professional Summary" — so it becomes a keyword:
Then 2–3 lines naming your strongest stack, your domain, and one quantified business outcome. Keep it under 50 words.
One of the highest-weighted sections for DS roles. Use a grouped, pipe-separated, plain-text format:
| Category | Example |
|---|---|
| Languages | Python | R | SQL | Scala |
| ML / DL frameworks | scikit-learn | TensorFlow | PyTorch | Keras | XGBoost | LightGBM |
| Data tools | Pandas | NumPy | Spark | Hadoop | Airflow | dbt |
| Visualisation | Tableau | Power BI | Matplotlib | Seaborn |
| Cloud & MLOps | AWS SageMaker | GCP | Azure ML | Docker | MLflow | Kubernetes |
| Methods | A/B Testing | Regression | Classification | Clustering | NLP | Computer Vision | Time Series | Feature Engineering |
This is where the bulk of your keyword weight lives. Use Action + Metric + Impact — and for DS specifically, the impact must be a business outcome, not just a model metric.
Full method: how to write resume bullet points that pass ATS.
For freshers, Projects is effectively your Work Experience.
➡ See how your sections are being read by ATS — free check on ResumeLens.
Data analyst, data scientist, and ML engineer are three different jobs with three different keyword sets — using the wrong one suppresses your score even when your skills are strong. Use these as a starting point, then refine against the specific JD.
| Role | Core ATS keywords |
|---|---|
| Data Analyst | SQL | Excel | Tableau | Power BI | Python (Pandas) | Data Visualisation | Dashboards | A/B Testing | Statistical Analysis | Reporting | Data Cleaning | ETL | Google Analytics | Business Intelligence | KPIs | Cohort Analysis |
| Data Scientist | Python | R | SQL | scikit-learn | Machine Learning | Regression | Classification | Clustering | Feature Engineering | A/B Testing | Hypothesis Testing | NLP | Time Series | Pandas | NumPy | Statistical Modelling | Predictive Analytics | Data Storytelling |
| ML Engineer | Python | TensorFlow | PyTorch | Model Deployment | MLOps | Docker | Kubernetes | AWS SageMaker | MLflow | Airflow | Feature Stores | Model Monitoring | CI/CD | Distributed Training | Spark | REST APIs | Inference Optimisation | Data Pipelines |
| Fresher / Entry Level | Python | SQL | scikit-learn | Pandas | NumPy | Machine Learning | Statistics | Data Visualisation | Matplotlib | Regression | Classification | Jupyter | Kaggle | Data Cleaning | Linear Algebra | Probability | EDA |
India-specific tip — Naukri Key Skills tags. Every Naukri.com posting lists "Key Skills" at the bottom — the exact terms the employer's system filters on. Make sure every tag that genuinely applies appears verbatim in your resume.
The most common DS resume mistake is listing models and accuracy scores without the business outcome or whether the model shipped. These bullets are everywhere:
Each fails twice. For ATS: thin on the role-specific keywords and context the scanner weights. For recruiters: a model's accuracy means nothing without knowing what it changed. Hiring managers read dozens of "92% accuracy" lines — what they remember is the candidate who deployed a model that moved a number the business cares about.
Rewrite every model bullet to name the technique, the metric, the deployment, and the business impact:
| Before | After |
|---|---|
| "Built a churn model with 92% accuracy." | "Deployed an XGBoost churn model (0.92 AUC) to production, cutting monthly churn 18% and protecting ₹1.2Cr in annual revenue." |
| "Worked on a recommendation system." | "Built a collaborative-filtering recommender served via SageMaker, lifting click-through 14% across 2M users." |
| "Did EDA and data cleaning." | "Cleaned and engineered features from 8M rows in Spark, cutting model training time 40% and improving F1 from 0.71 to 0.83." |
➡ Not sure if your DS bullets are ATS-optimised? Check your score on ResumeLens.
| Avoid | Do instead |
|---|---|
| Jupyter-notebook or portfolio-site exports (often image-based) | Word or Google Docs → text-selectable PDF |
| Skill radar charts / proficiency rings | Plain-text grouped skills list |
| Framework logo grids (Python/TensorFlow icons) | Skill names as text |
| Kaggle badges and GitHub contribution graphs | Plain-text Kaggle/GitHub URLs with rank stated in words |
| LaTeX equations or embedded charts as images | Describe the method in text |
| Two-column layouts and tables | Single-column, plain text |
Use safe fonts (Arial, Calibri, Garamond; 10–12pt body), standard headings (Work Experience, Skills, Projects, Education), and keep contact details in the body.
| Level | Lead section | Length |
|---|---|---|
| Fresher / new grad (0–1 yr) | Education + Projects (Kaggle/GitHub) | One page |
| Junior–mid (1–4 yrs) | Work Experience | One page |
| Senior / Lead (5+ yrs) | Summary, then Work Experience | Two pages (page one carries the most impact) |
➡ Check your ATS score and keyword gaps for free on ResumeLens.
A single-column, plain PDF built from Word or Google Docs. Use standard headings — Skills, Work Experience, Projects, Education — with a grouped, pipe-separated skills list and impact-driven bullets that name the technique, the metric, and the business outcome. Avoid two-column layouts, notebook exports, radar charts, and logo grids.
Freshers and under-3-years should keep to one page. Three to eight years can go to two pages if the content is strong. Senior and lead data scientists should use two pages, with everything important on page one.
Languages (Python, R, SQL), ML frameworks (scikit-learn, TensorFlow, PyTorch), data tools (Pandas, NumPy, Spark), visualisation (Tableau, Power BI), and cloud/MLOps (SageMaker, Docker, MLflow) — all as plain text, using exact JD terminology, and woven into your experience bullets, not just listed.
Yes, if your rating is meaningful — state your tier or rank in words ("Kaggle Expert, top 5%") and link your profile as plain text. Do not paste Kaggle medal badges or graphs; ATS cannot read them.
They target different keyword sets. Analyst resumes emphasise SQL, dashboards, and reporting; data scientist resumes emphasise modelling, statistics, and experimentation; ML engineer resumes emphasise deployment, MLOps, and pipelines. Use the set that matches the role you are applying for, and mirror the specific JD.
No. A PhD helps for research-heavy roles, but most industry data science and analyst roles value demonstrated projects, deployed models, and quantified business impact over the degree. If you have a relevant MSc or strong project portfolio, lead with those.
No. Canva exports design-layer or image-based PDFs that ATS parsers cannot read correctly — and data science templates are especially design-heavy. Build in Word, Google Docs, or Resume Builder and export a standard text-selectable PDF.
Related guides: How to Optimize Your Resume for ATS (Complete Guide) · ATS Resume Format for Software Developers · Resume Bullet Points That Pass ATS · What Is a Good ATS Score? · Free ATS Resume Checker
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