Data Scientist CV — example, template & tips for India 2026
Write an ATS-optimised data scientist CV that proves your ML skills with deployed models and business impact. Used by data scientists at top Indian and global tech companies.
Salary range (India)
₹8L – ₹60L depending on experience and specialisation
Top hiring companies
Google · Amazon · Microsoft Research · Flipkart · Ola
What to include in a Data Scientist CV
- ML frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost
- Projects with model accuracy, business impact, and deployment context
- Research papers or Kaggle competition rankings if applicable
- Statistics depth: hypothesis testing, regression, Bayesian methods
- Big data: Spark, Hadoop, BigQuery, Databricks
- MLOps: model deployment, monitoring, retraining pipelines
Common Data Scientist CV mistakes
- Listing ML libraries without deployment context — "trained a model" vs "deployed model serving 2M predictions/day"
- No business impact — academic accuracy metrics alone don't impress product companies
- Overloading with buzzwords (GPT, LLM, GenAI) without substance — be specific about what you built
- Missing statistics fundamentals — data scientists who can't explain p-values fail basic screening
Top skills for a Data Scientist CV
These keywords are heavily screened in ATS for data scientist roles. Make sure they appear naturally in your CV.
Pro tip: use CV Prime's AI to check your keyword coverage
Paste the specific data scientist job description you're applying to and our AI will score your keyword match, identify gaps, and rewrite your bullets to include the exact terms the ATS is looking for.
Check my keyword score freeData Scientist CV — frequently asked questions
Should a data scientist include Kaggle rankings on their CV?
Yes, if you are in the top 10% for any competition. A Kaggle Expert or Master title is a strong signal. For freshers, a well-documented Kaggle notebook is often more valuable than academic coursework.
How do I show GenAI/LLM experience on my data scientist CV?
Specify the model, use case, and impact. "Fine-tuned Llama 3 on domain-specific data, achieving 23% improvement in task accuracy, deployed via FastAPI serving 50K requests/day" is strong. Generic "worked with LLMs" is not.
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