What does it look like to work in data at L’Oréal?

Well, first it depends on your appetence! You could join either business-oriented projects (finance, marketing, supply chain…) or scientific-oriented projects in the R&I entity.

Let’s highlight the fact that positions at L’Oréal constantly evolve and adapt to our changing environment. But here is a sample of what you can do as a Data Scientist:

• Deployment of the predictive model in a tool/application that could be easily use by everyone (e.g., prediction of future consumer trends) to improve the decision-making process
• Continuous implementation and testing of different approaches, respecting the team’s principles in terms of code and methodologies
• Perform code reviews on various projects, as well as present your code to your peers for continuous review and improvement
• Exploratory analysis of a large database to extract insights from the data and work on the representation/visualization of results
• Active communication of the results with the business/scientific stakeholders

Let’s be concrete:

For Reasearch & Innovation departments, you could work on:

• Bayesian methods inventory (posterior probability; Bayesian power; Insurance; Bayes Factor; etc.)
• Development of an application on R Shiny (R&I): Calculation of decision-making indicators, calculation of number of subjects, meta-analyzes

For Tech Accelerator (our incubator) , you could work on:

• Construction of a similarity calculation engine between products on multiple parameters
• Creation of a recommendation engine for our professional customers to simplify the purchase of multi-brand products.

Data science is a perfect match for you if:

• Academic background: Master’s in data science, bio-statistic, statistics or applied mathematics
• Understand business stakes and have a business-oriented appetence
• You are curious and collaborative with a sense of popularization and argumentation
• Have a good level of English

Here are some more technical requirements:

• Python (unit test, docstrings documentation, object oriented…), SQL
• Knowledge in statistic (tests theory, descriptive statistics, probability, integration, - Bayesian analysis, mixt linear model)
• R/R shiny programming
• Technical and functional documentation skills

This could be a nice to have!

• Back-end (flask, Django, NodeJS) and front-end (ReactJS, VueJS) skills
• Used to Docker, Kubernetes, KubeFlow