Develop, validate and scale the Resource Allocation Model, quantifying the impact of Sales and Marketing packages on sales and brand performance.
Implement optimization algorithms to guide budget allocation and maximize marketing ROI across geographies and product portfolios.
Lead the development of predictive and prescriptive models to support commercial, trade and brand teams.
Use PySpark to manage and transform large volumes of data from retail, media and consumer behavior sources.
Build and deploy machine learning models using Python and TensorFlow, ensuring robust model performance and business relevance.
Collaborate with stakeholders in marketing, category and commercial teams to incorporate analytical insights into strategic decisions.
Use GitHub Actions for version control and CI/CD workflows, and DVC for data versioning and creation of reproducible machine learning pipelines.
Present results through data storytelling and dashboards targeted at senior leadership.
Mentor junior data scientists and contribute to a culture of innovation and excellence.

Bachelor's, Master's or PhD in a quantitative field (e.g., Data Science, Statistics, Computer Science or Economics).
More than 5 years of hands-on experience in Data Science, preferably in the FMCG or retail sectors.
Deep knowledge of optimization techniques (e.g., linear programming, genetic algorithms and constrained optimization).
Advanced programming skills in Python (pandas, scikit-learn, statsmodels, TensorFlow).
Experience in Machine Learning, including implementing supervised and unsupervised algorithms in production environments.
Experience with PySpark for processing and transforming large-scale distributed data.
Experience with Git and GitHub Actions for collaborative development and CI/CD pipelines.

Senior Data Scientist

Key skills