About us
AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets.
About AB InBev Growth Group
Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams to leverage data and drive digital transformation. The group supports global brands like Corona, Budweiser and Michelob Ultra, and manages digital products including the B2B platform BEES, Ze Delivery, and TaDa Delivery.
About BEES
The BEES AI organization drives data science and machine learning strategy across customer-facing products, logistics, fintech, and operations. We build end-to-end intelligent systems that optimize commercial execution, improve customer engagement, and enhance operational efficiency at global scale.
What you'll do
- Be part of a high-impact data science team building intelligent systems that support sales execution and customer engagement at a global scale.
- Design, develop, and deploy machine learning models and optimization solutions across the full lifecycle — from research and experimentation to production.
- Apply advanced techniques such as statistical modeling, clustering, optimization, and model explainability to generate actionable insights.
- Translate complex commercial and operational problems into scalable data science solutions.
- Lead and contribute to experimentation and performance evaluation, ensuring models are robust and aligned with business objectives.
- Write production-grade code and build reusable data and modeling pipelines.
- Collaborate with engineers, product managers, and business stakeholders to ensure solutions are effectively integrated into frontline tools.
- Ensure model transparency by leveraging explainability techniques and communicating model behavior to stakeholders.
What you'll need
- Strong foundation in mathematics, statistics, and problem-solving.
- Bachelor’s degree in a quantitative field; Master’s preferred; PhD is a plus.
- Proven experience applying machine learning, clustering, or optimization to real-world problems in production environments.
- Proficiency in Python for data analysis, modeling, and production workflows; experience with distributed processing (e.g., Spark / PySpark) is a plus.
- Experience with model explainability techniques (e.g., SHAP, feature importance) and dimensionality reduction methods.
- Experience with experimentation frameworks, model validation, and performance monitoring.
- Strong understanding of software engineering best practices, including version control and CI/CD.
- Excellent communication skills with the ability to explain complex models and trade-offs to non-technical audiences.