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, including North America, Latin America, Europe, Asia, and Africa.
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. 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 table top beer keg PerfectDraft.
We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.
About BEES
At BEES, our ambition is – and always will be – to put customers at the heart of everything we do, making their lives easier and their businesses more profitable. Through our B2B e-commerce and SaaS platform, we bring the power of digital to small and medium-sized retailers, unlocking new growth opportunities for all.
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.
As a member of the BEES Frontline Data Science team, you will develop data-driven solutions that power sales force effectiveness, customer coverage strategies, and execution consistency across markets.
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 — focusing on customer segmentation, visit planning, and execution strategy.
- Apply advanced techniques such as statistical modeling, clustering, optimization, and model explainability to generate actionable insights and improve decision-making.
- Translate complex commercial and operational problems into scalable data science solutions, incorporating business rules, constraints, and edge cases.
- Lead and contribute to experimentation and performance evaluation, ensuring models are robust, interpretable, and aligned with business objectives.
- Write production-grade code and build reusable data and modeling pipelines that operate reliably at scale.
- Collaborate closely with engineers, product managers, operations teams, and business stakeholders to ensure solutions are effectively integrated into frontline tools and processes.
- Drive technical excellence by exploring and applying state-of-the-art methodologies in machine learning, optimization, and analytics.
- Ensure model transparency and trust by leveraging explainability techniques and clearly communicating model behavior and trade-offs to stakeholders.
What you'll need:
- Strong foundation in mathematics, statistics, and problem-solving.
- Bachelor’s degree in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative field; Master’s preferred; PhD is a plus.
- Proven experience applying machine learning, clustering, optimization, or advanced analytics to real-world problems in production environments.
- Experience with complex systems involving uncertainty, business constraints, and large-scale structured and unstructured data.
- Proficiency in Python for data analysis, modeling, and production workflows; experience with distributed processing (e.g., Spark / PySpark) is a plus.
- Familiarity with at least one of the following domains: customer analytics, route-to-market strategy, or commercial operations.
- Experience with model explainability techniques (e.g., SHAP, feature importance, dimensionality reduction methods such as PCA) and interpreting model outputs for business use.
- Experience with experimentation frameworks, model validation, and performance monitoring.
- Strong understanding of software engineering best practices, including version control, CI/CD, and reproducible workflows.
- Ability to work with ambiguity, challenge assumptions, and translate complex business needs into structured analytical solutions.
- Excellent communication skills, with the ability to explain complex models, trade-offs, and insights to both technical and non-technical audiences.
What we offer
• Performance-based bonus*
• Attendance bonus*
• Private pension plan
• Meal allowance
• Casual office and dress code
• Days off*
• Health, dental, and life insurance plans
• Discounts on medications
• Partnership with WellHub
• Childcare assistance
• Discounts on Ambev products*
• Clube Ben partnership
• Scholarship program*
• School supplies support
• Language learning platforms and training
• Transportation allowance
*Applicable rules apply.
Skills Required
- Strong foundation in mathematics, statistics, and problem-solving
- Bachelor's degree in Mathematics, Statistics, Engineering, Computer Science, or related quantitative field
- Master's degree in a related field
- PhD in a related field
- Proven experience applying machine learning, clustering, optimization, or advanced analytics in production
- Experience with complex systems involving uncertainty, business constraints, and large-scale structured and unstructured data
- Proficiency in Python for data analysis, modeling, and production workflows
- Experience with distributed processing (e.g., Spark / PySpark)
- Familiarity with customer analytics, route-to-market strategy, or commercial operations
- Experience with model explainability techniques (e.g., SHAP, feature importance, PCA)
- Experience with experimentation frameworks, model validation, and performance monitoring
- Strong understanding of software engineering best practices, including version control, CI/CD, and reproducible workflows
- Excellent communication skills and ability to translate complex models to non-technical stakeholders
What We Do
We are the world’s leading brewer bringing people together for a better world. For centuries, the experience of sharing a beer has brought people and cultures together. Even in our hyper-connected, always-on world, this simple act is as meaningful today as it was generations ago. We are AB InBev. Committed to driving growth that leads to better living for more people in more places. Through brands and experiences that bring people together. Through our dedication to brewing the best beer with the best ingredients. And through our commitment to helping farmers, retailers, entrepreneurs, and communities grow. We are building a company to last. Not just for a decade. But for the next 100 years. Through our brands and our investment in communities, we will bring more people together, making our company an integral part of our consumers’ lives for generations to come. Our diverse portfolio of well over 500 beer brands includes global brands Budweiser, Corona and Stella Artois; multi-country brands Beck’s, Castle, Castle Light, Leffe and Hoegaarden; and local champions such as Aguila, Antarctica, Bud Light, Brahma, Cass, Chernigivske, Cristal, Harbin, Jupiler, Klinskoye, Michelob Ultra, Modelo Especial, Quilmes, Victoria, Sedrin, Sibirskaya Korona, and Skol. Anheuser-Busch InBev is a publicly traded company (Euronext: ABI) based in Leuven, Belgium, with secondary listings on the Mexico (MEXBOL: ANB) and South Africa (JSE: ANH) stock exchanges and with American Depositary Receipts on the New York Stock Exchange (NYSE: BUD).
