# Data scientist CV example and résumé tips

> A data scientist CV example showing how to present machine learning projects, Python skills and business impact, with keywords and interview questions.

Updated 2026-10-07 by Litjob. Web version: https://getlitjob.com/cv-examples/data-scientist

Strong data science CVs connect models to money, time or risk. This example shows a data scientist who explains what each model did for the business, not just which algorithm it used.

## Sample CV

The person and companies below are fictional.

### Lukas Weber, Data Scientist

Data scientist with 4 years of experience building forecasting and recommendation models in Python. I take models from notebook to production and measure them against business outcomes, not just accuracy scores.

#### Experience

**Data Scientist, Freshcart Delivery** (Berlin), 2023 to present

- Built a demand forecasting model (LightGBM) for 300 products across 12 warehouses, reducing food waste by 18%.
- Shipped a recommendation model that increased basket size by 5% in an A/B test with 200,000 users.
- Set up model monitoring that flags data drift weekly, catching two silent failures before they affected customers.

**Junior Data Scientist, Insurely** (Hamburg), 2021 to 2023

- Developed a claims-fraud classifier that helped investigators focus on the riskiest 5% of claims.
- Built a churn model and presented the main drivers to the retention team, who redesigned renewal emails as a result.

#### Education

- M.Sc. Data Science, Technical University of Munich (2021)
- B.Sc. Mathematics, University of Hamburg (2019)

#### Skills

- Modelling: Regression, Gradient boosting, Time series, Recommendation systems, Experiment design
- Tools: Python, SQL, scikit-learn, PyTorch, MLflow, Airflow
- Cloud: AWS SageMaker, Docker, BigQuery

## Data scientist summary examples

**New graduate:** M.Sc. graduate in data science with a thesis on demand forecasting and 2 Kaggle competition medals. Comfortable with Python, SQL and scikit-learn, and keen to put models into production.

**Senior:** Senior data scientist with 8 years leading pricing and personalisation work. Built the experimentation platform used by 6 product teams and mentor 4 data scientists.

## Skills for a data scientist CV

- Hard skills: Python (pandas, NumPy, scikit-learn), SQL, Statistics and experiment design, Machine learning (supervised and unsupervised), Deep learning, if the role needs it (PyTorch or TensorFlow), Deploying and monitoring models (MLOps), Data visualisation
- Soft skills: Translating business problems into data problems, Communicating uncertainty, Pragmatism over perfection, Collaboration with engineers (show these through your bullet points)

## Keywords recruiters and ATS look for

data scientist, machine learning, Python, SQL, statistics, predictive modelling, A/B testing, scikit-learn, deep learning, MLOps, NLP, forecasting

Compare your CV with a specific job ad using the free CV keyword checker: https://getlitjob.com/cv-keyword-checker

## Bullet points: before and after

- Weak: Built machine learning models.
  Stronger: Built a churn model that identifies at-risk customers 30 days early, now used by the retention team.
- Weak: Used Python for data analysis.
  Stronger: Analysed 3 years of pricing data in Python and recommended changes that lifted margin by 2 points.
- Weak: Worked on NLP.
  Stronger: Fine-tuned a text classifier that routes 70% of support tickets automatically.
- Weak: Deployed models.
  Stronger: Moved 4 models from notebooks to a scheduled pipeline (Airflow, MLflow) with weekly drift checks.

## Tips for data scientist CVs

### Measure models in business terms

Accuracy and AUC matter to other data scientists. Revenue, cost, time saved and risk reduced matter to the person hiring you.

### Show you can ship

Many candidates only show notebooks. One bullet about deployment, monitoring or working with engineers sets you apart.

### Keep projects relevant

Choose portfolio projects close to the company’s domain. A forecasting project fits retail; a fraud project fits fintech.

### List methods you can defend

Interviewers will ask how a method works. Only list techniques you can explain and have used for real.

## Data scientist interview questions

### How would you explain your model to a non-technical manager?

Focus on what it predicts, how good it is in business terms, and its main limitation.

### How do you handle imbalanced data?

Mention resampling, class weights, choosing the right metric (precision, recall) and threshold tuning.

### Your A/B test shows a 2% lift. Do you ship it?

Talk about significance, sample size, practical impact, guardrail metrics and novelty effects.

### Tell me about a model that did not work.

Show what you learned and how you found out, ideally before it hurt users.