💻 Tech

Data Scientist — CV Example

A template for data scientists who turn data into decisions and models into revenue.

Elena Petrova

Data Scientist

📍 Cambridge, UK✉️ elena.petrova@email.com

Summary

Data Scientist with 4 years of experience building ML models for fraud detection, recommendation systems, and demand forecasting. PhD in Statistics with production experience deploying models at scale using Python and AWS.

Work Experience

Senior Data Scientist at Arm HoldingsAug 2022 — Present

Build demand forecasting models reducing inventory costs by £12M annually across semiconductor supply chain
Deploy ML pipelines on AWS SageMaker processing 50M+ records daily for real-time predictions

Data Scientist at Monzo BankSep 2020 — Jul 2022

Developed fraud detection model reducing false positives by 45% while maintaining 99.2% recall
Built recommendation engine for financial products increasing cross-sell conversion by 28%

Skills

Python / RMachine LearningAWS SageMakerSQL / SparkDeep Learning (PyTorch)Statistical Modelling

What Recruiters Look For

Data Scientist CVs must show the full pipeline: from data exploration to model deployment. Recruiters want to see the business problems you solved, the models you built, and the measurable impact on revenue, costs, or user experience.

Key Skills to Include

Python, R, SQL, machine learning, deep learning (PyTorch/TensorFlow), cloud platforms (AWS SageMaker, GCP Vertex), statistical modelling, A/B testing, and data visualisation.

Common Mistakes

Focusing too much on tools and not enough on impact. Building a model is not an achievement. Building a model that reduced fraud losses by 45% while improving customer experience is an achievement.

Formatting Tips

One to two pages. Include publications and a GitHub link if relevant. Use a Technical Skills section grouped by category. Lead with your most impactful project.

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