AI/ML Applied Scientist - BEES Growth Group

About AB InBev Growth Group

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, including North America, Latin America, Europe, Asia, and Africa.

The Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well-known global beer brands like Corona, Budweiser, and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and tabletop beer keg PerfectDraft.

About the Role

The Team: BEES is the team driving AI strategy at BEES by building end-to-end products to serve our teams, customers, and partners across the globe. The organization is a cross-functional blend of AI/ML teams from Applied Research and Machine Learning Engineering to Machine Learning Platform Product Management. Together, the team is responsible for building the tools and products needed to deliver world-class AI/ML capabilities.

As a member of the BEES Applied Science team, you will develop solutions for challenging business problems in areas such as portfolio optimization, recommendations, personalization, forecasting, segmentation, and insights discovery.

What You Will Do

• Collaborate on the design of complex analysis of datasets and the development and deployment of advanced machine learning and deep learning algorithms for portfolio optimization, recommendations, personalization, promos, segmentation, imputation, and insight discovery. Ensure that solutions are robust, scalable, and aligned with business objectives while mentoring junior and mid-level data scientists.
• Actively stimulate the machine learning and deep learning updates on advancements, continuously incorporating cutting-edge techniques to solve complex business problems. Champion the adoption of frontier algorithms to drive innovation and technical excellence across the team’s initiatives.
• Architect and optimize large-scale data processing workflows on cloud platforms such as Azure, Databricks, and Spark, ensuring performance and scalability. Guide best practices in leveraging these platforms to accelerate delivery.
• Support the design and implementation of reliable data pipelines and modeling workflows using Python and PySpark. Introduce automation and reusable components that enhance team productivity and reduce operational overhead.
• Engage proactively with cross-functional teams—including data engineering, product management, and business stakeholders—to translate business needs into technical requirements and deliver impactful, end-to-end solutions. Foster an agile mindset, driving rapid iteration and continuous improvement.

Qualifications

• A Bachelor’s degree in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative field; Master’s degree preferred; PhD a plus.
• Demonstrated success applying machine learning and statistical modeling techniques in production environments to drive measurable business impact.
• Communication skills, with the ability to translate complex technical concepts into clear, actionable insights for both technical and non-technical stakeholders.
• Experience or strong familiarity with experimentation frameworks, model validation, and rigorous evaluation, ideally in high-stakes decision systems.
• Proven ability to apply machine learning and statistical models in production, balancing accuracy, stability, explainability, and real-world constraints.
• Proficiency in Python for analysis, modeling, and production workflows; experience with distributed data processing (e.g., Spark / PySpark) is a plus.
• Experience with modern ML development practices, including version control, CI/CD, and reproducible research and modeling workflows.
• Strong proficiency using AI coding agents and developer tools in a controlled, thoughtful, and production-oriented way to accelerate development without compromising quality.
• Creative, curious, and independent thinker who challenges assumptions, proposes novel approaches, and reasons deeply about complex systems.
• Excellent communication skills, able to clearly explain complex models and results to both technical and non-technical audiences.