ジョブファンクション: インフォメーション システム（IS)
Within the Beauty Tech Factory, the Tech Accelerator has to support and accelerate the transformation of L’Oréal on new technologies: Data Science & AI, IoT, Blockchain, UX / UI...
You will join the Data Science team for Supply chain use cases, whose defines the Group' Data Science & AI standards and implement them by delivering strategic and high priority projects for the group.
Its 4 main missions are:
Ensure the Data Science developments of Supply chain projects and develop scalable tech assets, starting with Demand Sensing project
Enrich a portfolio of data & analytics micro services at a global level
Stimulate IT transformation by applying standards in terms of methodology, code, MLOps…
Manage an ecosystem of data science & tech partners and ensure high level of expertise
Data Culture :
Build, lead and orchestrate the global data science community
Scale and share technical assets
Tech Academy :
Ensure consistency of HR strategy through strong support for recruitments & academic partnerships
Contribute to learning journey definition and contents
Diversity of Data (internal / external, structured or not) and missions (marketing, supply, retail, ...) ;
As a Data Scientist / ML Engineer you will use Machine learning approaches, with huge code quality and engineering skills to develop Data Science and AI products.
You will contribute to the design and development of Data Science solutions for strategic business cases with support of external Tech partners, and ensure the consistency of Tech Accelerator guidelines within these projects.
Your mission will be to develop Demand Sensing solution at group level with the main missions bellow:
Increase demand forecast accuracy by developing models, with huge code quality, based on time series, internal and external features ;
Challenge external Tech partners on their code and approaches in an agile delivery squad ;
Ensure consistency of Tech Accelerator standards within the squad ;
Share Tech Accelerator standards with L’Oréal Data Science community ;
Tech skills :
Experience applying ML / DL to solve challenging problems ;
Knowledge and practical experience on a project or proof of value ;
Experience working effectively with engineering teams ;
Programming skills : Python, Tensorflow, PyTorch, Keras, Spark or other frameworks ;
Excellent verbal and written communication (French and fluent English) and presentation skills, ability to convey technical concepts and their implications to non-experts
Flexibility and open-mindedness; alertness and argumentation; entrepreneurial spirit; relationship skills;
Collaborative mindset with different Tech profiles (data scientists, engineers & architects) and business (data owners & stewards, business owners)