<p> </p> <p><span style="font-size: 12pt;"><strong>About us</strong></span></p> <p>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.</p> <p><span style="font-size: 12pt;"><strong>About AB InBev Growth Group</strong></span></p> <p>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.</p> <p>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.</p> <p>We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.</p> <p><span style="font-size: 12pt;"><strong>About BEES</strong></span></p> <p>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.</p> <p>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.</p> <p>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.</p> <p> </p> <p><strong>What you'll do:</strong></p> <ul> <li>Implement and maintain <strong>individual components</strong> of the data platform—for example, ingestion jobs, dbt models, Spark transformations, CDC tasks, matching rules, or deduplication logic.</li> <li>Make <strong>implementation decisions within a component</strong>: schema mapping, transformation logic, join strategy, and similar choices bounded to that unit of work.</li> <li><strong>Fix defects</strong> in transformations, ingestion jobs, or entity resolution logic when issues are identified.</li> <li>Ensure <strong>component outputs match</strong> the expected schema, data contracts, and downstream expectations.</li> <li><strong>Improve</strong> a component’s performance, data quality checks, or reliability when gaps or incidents require it.</li> <li><strong>Follow existing ETL and MDM standards</strong> and team patterns rather than inventing parallel approaches.</li> <li><strong>Apply security and compliance expectations</strong> to your components: handle sensitive and personal data according to <strong>classification, retention, and minimization</strong> rules; avoid logging, samples, or exports that <strong>over-collect or expose</strong> regulated fields beyond what the use case requires.</li> <li><strong>Use approved identity, access, and secrets patterns</strong> for jobs and services (for example, role-based access, managed identities, or vault-backed credentials)—<strong>not</strong> hard-coded secrets or ad hoc shared accounts.</li> <li><strong>Support auditability</strong> of changes and data movement as the team defines it (for example, clear job ownership, metadata, lineage hooks, or evidence packs for controls) so security and compliance reviews can trace what the pipeline does.</li> </ul> <p><span data-teams="true"><strong>What you'll need:</strong></span></p> <ul> <li>Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Systems Analysis and Development, or similar.</li> <li>Intermediate English.</li> <li><strong>Code quality:</strong> write clear, readable, modular code; follow team naming and formatting conventions; avoid unnecessary duplication in your own changes; prefer changes that can be understood without a verbal walkthrough.</li> <li><strong>Verification:</strong> add required unit or transformation-level tests; validate schema assumptions and basic data quality conditions; ensure changes do not break existing behavior.</li> <li><strong>Delivery:</strong> submit well-structured pull requests that include a clear description of the change, context, and expected impact, and evidence of testing.</li> <li><strong>Stack (typical):</strong> Python, SQL, and data processing with <strong>PySpark and/or Scala</strong> as used in the team’s pipelines.</li> <li><strong>Pipelines:</strong> practical experience building or maintaining batch/stream components with orchestration (for example, Apache Airflow, Databricks Workflows, or similar) and version control (Git).</li> <li><strong>Data work:</strong> comfortable with transformation,