Manager - Advanced Analytics; CDMO

Title: Manager
- Advanced Analytics

Division: Corporate, CDMO

Reports To: AVP – Advanced Analytics

Location: New York, NY

Department: CDMO
- Consumer Function
- Market Intelligence Team (CMI)

For more than a century, L’Oréal has devoted its energy, innovation, and scientific excellence solely to one business:
Beauty. Our goal is to offer each and every person around the world the best of beauty in terms of quality, efficacy, safety, sincerity and responsibility to satisfy all beauty needs and desires in their infinite diversity.

The Corporate Digital and Marketing Office mission is to put the consumer at the heart of L’Oréal’s business and drive digital innovation. As a force for L’Oréal’s innovation, CDMO delivers the best consumer experiences, drives new marketing models, and spearheads new digital capabilities to future‑proof the group’s business.

Responsibilities will include but are not limited to building models for forecasting, enhancing our A/B testing tool, enhancing our MMM tool to build additional capabilities such as Network analysis, propensity models, market basket analysis. Hence a working knowledge of some of the statistical and modeling techniques such as Regression (Linear/Logistic/Bayesian), Time series models (ARIMA/ARIMAX, SARIMA/SARIMAX), ML (Decision Trees, Random Forest, Clustering) etc.

will be crucial.

An ideal candidate will possess a sharp analytical mindset and have the capability to clearly articulate and apply analytical learnings for business partner situations. Strong communication skills (both oral and written), comfortable working across multiple work‑streams and stakeholders simultaneously, a flexible/adaptable mindset, and a patient approach to train others within the business teams (CDMO/MI, media teams, brand teams, financial teams, global teams) on the tool’s inputs and applications.

Optimal candidates will also possess strong project management skills – timeline management, role/responsibility assignments, task execution – for efficient and effective project implementation.

• Work with the Advanced Analytics team and the business stakeholders to understand the problem statement and identify the modeling technique most appropriate to model it.
• Work with the IT team (and external consultants if needed) to build the model, validate it and deploy it.
• Translate the insights derived from the models into business‑friendly presentations.
• Use L’Oréal provided pre‑built tools to build models and generate insights.
• Interview business stakeholders to understand the problems and identify areas where data‑driven insights would drive the business forward.

• Experience in statistical modeling, marketing research and business strategy.
• Successful track record of delivering models based on techniques such as Regression, Time Series Forecasting, ML, etc.
• Proficiency with Python, SQL, R.
• Experience implementing and using ELT/ETL pipelines within a Google Cloud Platform (GCP) environment.
• Ability to build and interact with RESTful APIs and containerization (Docker).
• Optimize cloud computing and storage resources for cost‑effective data science workloads.
• Implementing secret management to protect sensitive data using API keys and credentials that adhere to data governance practices.
• Successful track record of synthesizing strategic business knowledge with data analysis/insights; capable of driving business strategy and decisions using analytics.
• Specific marketing response modeling expertise such as MMM (Marketing Mix Modeling) and business application.
• Cross‑functional skills – can explore different business viewpoints and work with teams to develop mutually owned solutions.
• Can solve problems in complex situations using creative and conceptual reasoning.
• Strong oral and written communication skills, extremely well‑organized; a leader in project management.
• Able to communicate complex marketing measurement and research results to a general audience.
• Can build strong documents (in PowerPoint) to visualize and story‑tell complex research results and communicate those learnings to end‑users.
• Exposure to large data sets (“big data”)…