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Resume example

Data scientist resume example

A data scientist builds statistical and machine learning models that answer a business question: which customers are likely to cancel, how much stock each warehouse needs next month, which insurance claims deserve a second look.

The work starts with a lot of data exploration in Python or R, continues with feature engineering and model training (scikit-learn, XGBoost, PyTorch), and ends with the part that decides whether the project survives: explaining the results to managers and getting the model into production with the data engineers. A growing share of projects now involves large language models, from document classification to internal assistants. In France, data scientists work in banks, insurers, retailers, energy companies, consulting firms and start-ups, and every project touching personal data has to pass the GDPR checks of the company's data protection officer.

Resume example

[First name Last name]

Data Scientist | Python, Machine Learning, Forecasting

[City] · [phone] · [email]

Profile

Data scientist with [x] years in retail and energy. I build demand forecasting and pricing models, work with data engineers to deploy them, and monitor their drift once in production. I translate model outputs into decisions that operational teams can use.

Experience

Data Scientist, [Company], [City]

[month year] to present

  • Built a weekly demand forecasting model for [x] stores, replacing a spreadsheet method used by the supply team
  • Designed and analysed A/B tests for pricing changes with the e-commerce team
  • Developed a text classification model to route customer emails, then moved it to production with the data engineering team
  • Set up drift monitoring and a monthly review of model performance with business owners
  • Wrote the documentation required by the data protection officer for each model using personal data

Resume headlines

  • Data Scientist | Python, Machine Learning, Forecasting
  • Data Scientist NLP | LLMs, Text Classification and Retrieval
  • Senior Data Scientist | Pricing and Risk Models for Insurance

Summaries for your profile

Entry level

Graduate of a master's in statistics and machine learning, with a six-month internship at [Company] where I built a churn model for the subscription team and presented the results to the marketing director. Strong in Python, SQL and model evaluation.

Experienced

Data scientist with [x] years in retail and energy. I build demand forecasting and pricing models, work with data engineers to deploy them, and monitor their drift once in production. I translate model outputs into decisions that operational teams can use.

Career change

Former actuarial analyst with a strong background in statistics and risk modelling. Over the past year I moved from SAS to Python, completed projects in gradient boosting and NLP, and now want to apply these methods to wider business problems.

Tips

  • For each project, state the business question, the method and what the business did with the result.
  • Show that at least one model reached production, or explain what stopped it.
  • Add a GitHub link or a short portfolio with notebooks that are readable without you.
  • Mention your degree's statistical content: French recruiters pay attention to the school or master's programme.
  • If you worked with LLMs, describe the use case and the evaluation, not only the model name.

Mistakes to avoid

  • Listing algorithms without any business context.
  • Reporting model accuracy alone, without saying whether it was useful.
  • Presenting Kaggle competitions as if they were professional experience.
  • Ignoring data protection, which French employers in banking, insurance and health expect you to take into account.

Working as a data scientist in France

Data scientist is not a regulated job in France. Employers usually expect a Bac+5: an engineering school, a statistics school such as ENSAE or ENSAI, or a university master's in mathematics, statistics or data science. Doctoral graduates are welcome in research teams, and CIFRE doctorates connect PhD work with a company. Any model using personal data must comply with GDPR, overseen by the CNIL, and high-risk AI systems now fall under the European AI Act. Search with « data scientist », « ingénieur machine learning », « ingénieur IA » and « chargé d'études statistiques ».

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Frequently asked questions

Do I need a PhD to work as a data scientist in France?

No. Most positions ask for a Bac+5 from an engineering school or a master's in statistics, mathematics or computer science. A PhD helps for research-oriented roles in large labs or in deep learning teams, and some candidates prepare one through a CIFRE contract with a company, but it is not the usual route.

Should I show academic projects on my resume?

Yes if you are a graduate, but present them like real work: data used, problem solved, method, result. Pick two or three relevant ones rather than every course project, and make sure the code is available if you link to it.

How do I present an LLM project?

Describe the task, the data, how you measured quality and what happened in production. Recruiters see many chatbot demos, so the evaluation method and the handling of errors and confidential data are what make your project stand out.

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