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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.
As a member of 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.
Design and deploy ML and deep learning models for portfolio optimization, recommendations and personalization
Mentor junior and mid-level data scientists
Architect and optimize large-scale data processing workflows on Azure, Databricks and Spark
Build reliable data pipelines and modeling workflows in Python and PySpark
Translate business needs into technical requirements with data engineering and product teams
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 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.
the development and deployment of advanced machine learning and deep learning algorithms for portfolio optimization, recommendations, personalization, promos, segmentation, imputation, and insight discovery
while mentoring junior and mid-level data scientists
Architect and optimize large-scale data processing workflows on cloud platforms such as Azure, Databricks, and Spark
Support the design and implementation of reliable data pipelines and modeling workflows using Python and PySpark
to translate business needs into technical requirements
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.
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.
Excellent communication skills, able to clearly explain complex models and results to both technical and non-technical audiences.
Bachelor’s degree in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative field; Master’s preferred; PhD a plus.
Proficiency in Python for analysis, modeling, and production workflows; experience with distributed data processing (e.g., Spark / PySpark) is a plus.
Qualifications - Bachelor’s degree in Mathematics, Statistics, Engineering, Computer Science, or a related quantitative field; Master’s preferred; PhD a plus. - Demonstrated success applying machine learning and statistical modeling techniques in production environments to drive measurable business impact.
From the employer’s posting.
AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies.
In the employer’s words · Read in context
Actively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.
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Day 6
Early. Similar jobs usually come down between day 7 and day 55.
From 9,988 similar postings that came down. Coming down is not always a hire.
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- 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.
- 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.
- Actively stimulate the machine learning and deep learning update 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.
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We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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.
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 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:
Qualifications
What We Offer:
*Rules applied
Equal Opportunity & Affirmative Action:
AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.
The following fields are optional, but anticipate the information for your registration*.
Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company.
For more information: www.abinbev.com
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