Data Science Resume Guide for India — ATS Keywords & Format (2026)

Data Science Job Market in India in 2025

India has plenty of data science graduates. What's hard to find are candidates who combine the technical skills with business sense and clear communication.

The competition is fierce. A data science role at a top Indian tech company can draw thousands of applications, and ATS screening removes most of them automatically. This guide is about making it into the group a person actually reviews.

Three Data Science Roles, Three Different Resumes

Before you write anything, decide which of these roles you're going for.

Data Science Resume Structure

Skills section: put it first

For data roles, put Skills right after your summary. Recruiters and ATS systems search for tool names.

Programming: Python (pandas, NumPy, scikit-learn, TensorFlow, PyTorch), R, SQL

ML/AI: supervised learning, deep learning, NLP, computer vision, time series, recommendation systems

Data Engineering: Apache Spark, Airflow, dbt, BigQuery, Snowflake

Visualisation: Tableau, Power BI, matplotlib, seaborn, Plotly

Cloud & MLOps: AWS (SageMaker, S3, EC2), GCP (BigQuery, Vertex AI), Docker, MLflow, CI/CD

Databases: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch

Statistics: hypothesis testing, A/B testing, regression analysis, Bayesian methods

Experience bullets: the right format

Every data science bullet needs:

• The model, algorithm or tool you used (for ATS keyword matching)
• The size of the data (rows, users, transactions)
• The metric you improved (accuracy, CTR, revenue, latency)
• The business impact (revenue, cost, retention)

Projects (critical for freshers and career changers)

Every project should include:

• Where the dataset came from and how big it is
• The full pipeline (data collection → preprocessing → modelling → evaluation → deployment)
• The metric you reached (accuracy, F1, RMSE, AUC-ROC)
• How that compares with a baseline

Customer Churn Prediction, Telecom Dataset | Python, XGBoost, SHAP

• Processed 500K customer records with 40+ features using pandas and scikit-learn
• Trained XGBoost classifier achieving AUC-ROC of 0.91 vs baseline of 0.71
• Applied SHAP values for model interpretability; identified top 5 churn drivers
• Deployed as REST API using FastAPI and Docker; model served 10K+ predictions/day

ATS Keywords by Data Role

SQL, Python, Excel, Power BI, Tableau, data cleaning, ETL, dashboard, KPIs, cohort analysis, A/B testing, reporting, stakeholder communication, data storytelling, Google Analytics, business intelligence

Python, machine learning, deep learning, scikit-learn, TensorFlow, PyTorch, NLP, computer vision, feature engineering, model evaluation, cross-validation, A/B testing, statistical modelling, hypothesis testing, pandas, NumPy, Spark, SQL

Python, MLOps, model deployment, Docker, Kubernetes, FastAPI, Flask, AWS SageMaker, GCP Vertex AI, MLflow, CI/CD, REST API, model monitoring, data pipeline, Apache Airflow, feature store, model serving

Certifications That Matter in India

• Google: Professional Data Engineer, Professional ML Engineer
• AWS: Machine Learning Specialty, Data Analytics Specialty
• Coursera: DeepLearning.AI specialisations (Andrew Ng), IBM Data Science
• Kaggle: Competition medals (Expert or higher carries real weight)
• DataCamp: Python for Data Science, SQL Fundamentals

Kaggle is worth a special mention. At data-heavy companies like Flipkart, Meesho, Swiggy and PhonePe, a Kaggle Expert or Master ranking is taken seriously.

Check Your Data Science Resume

For every application, run your resume against that JD on ATSPass. Data science JDs are very specific about tools and frameworks. A resume tuned for a "Python + scikit-learn" role can score poorly against a "PySpark + Databricks" JD, even if both skills are on it.