Reporting to the Director of  Consumer Data & Data Science Director,  will be responsible for enabling brands to delight consumers and helping them express their inner selves through personalized interactions with L'Oréal brands throughout all the phases of their life. They will support this vision by continually increasing quality and efficiency of support services, elevating data literacy and access across the organization, and leading key strategic data projects to advance the state of L’Oreal’s data practice.

Responsibilities:

Clearly communicate and build support for the 5-year data practice vision

Work closely with brands, IT, and local and international executive leadership to modernize key systems

Accelerate the data driven marketing transformation by increasing data accessibility and upskilling brands on data analysis

Collaborate with business leaders across the organization to define objectives for CRM performance and a roadmap for short-term data practice evolution

Manage development of reporting and insights suite to maximize business decision quality while minimizing low-value work

Lead a technical team to produce high quality insights to over 20 brands across four divisions

Manage internal and external resources to deliver audiences and data extracts in a secure and efficient way

Work with global leadership team to build and scale innovative ML and AI projects efficiently and in close collaboration with local business leaders

Continuously bring forward relevant models and solutions to address key challenges faced across brand business units

Collaborate closely with CRM & Loyalty manager to help brands improve consumer journeys and their loyalty practice

Qualifications:

University degree with focus in business strategy or data analytics

5+ years’ experience in a data & analytics related role

Team leadership at least 1 year

Experience working with CRM analysis

Experience with data visualization software, DOMO preferred

Strong analytical and algorithmic thinking skills

Sufficient expertise of Python, R, and SQL to support technical developers in passing development roadblocks

Comfortable presenting complex technical subjects to non-technical brand and executive leadership in a simple and inspiring way

Strong understanding of different ML & AI model types, how to use them, as well as their various strengths and weaknesses