Who are we?
At L’Oréal, there is never a dull day, the beauty lies in the freedom to go beyond with our empowering entrepreneurial culture. We take pride in developing young talents who have the passion and ambition to make an impact in the beauty industry. We currently offer a wide range of career opportunities for undergraduates and fresh graduates across the region.
SAPMENA (South Asia Pacific, Middle East & North Africa) is home to 40% of the world's population & some of the fastest-growing economies. Headquartered in Singapore, we have over 6,800 diverse talents in 15 subsidiaries with 34 International Brands, 4 factories & 2 research centres. As the leading beauty tech company, we offer endless exciting career opportunities.
Join us on our mission to: Create the beauty that moves the world.
Watch our video here to find out what it’s like working for L’Oréal!
In your role, you will be expected to understand business challenges and and build data products/statistical modelling solutions to tackle them. You will have a critical role working with our stakeholders to make recommendations to the business.
- Explore and choose the algorithms which have best performance
- Develop, train and finetune the model
- Work with ML engineers to support the industrialization
- Responsible for engaging with key stakeholders within the organization, the Delivery Team, Data Engineers, BI Developers, analytics analyst, IT PMO and business to build an exceptional product.
- Working closely with a product owner and effectively captures stakeholder assumptions and translates them into hypothesis driven outcomes.
- Be accountable for the end-to-end results and delivery of your product
- Collaborate with stakeholders on model/product launch communication, training and enablement and ongoing product adoption tactics, where applicable.
- Support and champion, where applicable, ongoing initiatives driving data analytics culture
- Act as a subject matter expert and provide guidance to the project team throughout the project. This includes (but not limited to):
- Knowing which sources of data to explore
- Identifying and communicating the key trends, data limitations
- Support scoping of the business problem out so that the project team can determine the best model/analysis/solution
- Assess reasonability of the data and/or model prior to sharing with the key business stakeholders