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Data Scientist — CV Example

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

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What Does a Data Scientist Actually Do?

A Data Scientist extracts insight from complex datasets using statistics, machine learning, and programming to help businesses make better decisions. Day-to-day work involves cleaning and exploring data in Python or R, building and evaluating predictive models, presenting findings to stakeholders, and working with engineers to deploy models into production. You'll work at tech companies, banks, retailers, healthcare organisations, and consultancies. You typically report to a Head of Data Science or Chief Data Officer and collaborate closely with data engineers and product managers.

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.

Average Salary — Data Scientist

United States
$100,000 – $145,000
United Kingdom
$60,000 – $90,000
Germany
$65,000 – $95,000
UAE / Dubai
$65,000 – $100,000
Canada
$80,000 – $115,000
Australia
$90,000 – $125,000

Figures in USD. Ranges reflect mid-level experience (3–7 years). Senior roles and major metro areas typically sit at the top of these bands.

Top 5 Interview Questions — Data Scientist

1Walk me through a machine learning project from problem framing to production deployment.
Show the full lifecycle: define the business problem, source and clean data, choose and train model, evaluate with appropriate metrics, handle imbalanced classes or data drift, and explain how it was deployed and monitored.
2How do you handle missing data and what factors affect your decision on treatment?
Cover the different types — MCAR, MAR, MNAR — and explain the trade-offs between deletion, mean/median imputation, and model-based approaches like MICE. Show you think about the downstream effect on model performance.
3Explain overfitting and how you prevent it in practice.
Go beyond "use train/test split". Talk about cross-validation, regularisation, feature selection, early stopping for neural networks, and learning curves as a diagnostic tool. Give an example if you can.
4How do you explain a complex model's output to a non-technical stakeholder?
Show communication skill alongside technical depth. Talk about SHAP values, partial dependence plots, and how you translate model outputs into business language with clear confidence intervals.
5Describe a time your analysis changed a business decision.
This is the most important question in the interview. Pick a concrete example with a measurable outcome. Show you can connect statistical work to real commercial impact.

How to Tailor Your CV

Google DeepMind, Meta, and Amazon want exceptional Python skills, strong ML fundamentals, and experience with large-scale distributed data systems like Spark or BigQuery. Consultancies like McKinsey QuantumBlack want both technical depth and client-facing communication skills. For scale-up and growth-stage startups, breadth matters — show you can work across the full stack from SQL queries to deploying a Flask API, not just build models in a Jupyter notebook.

Related CV Examples

💻Data Analyst→💻Machine Learning Engineer→💻Business Analyst→

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