Senior Data Scientist Portfolio - BEES Personalization
External
Abinbev · Campinas, BrazilPrepare for this interview
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About the role
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.
Responsibilities
- Collaborate on the design of complex analysis of datasets and the development and deployment of advanced machine learning algorithms for portfolio optimization, recommendations, personalization, improve business economics and insight discovery.
- Ensure that solutions are robust, scalable, and aligned with business objectives. Support the design and implementation of reliable data pipelines and modeling workflows using Python and PySpark.
- Architect and optimize large-scale data processing workflows on cloud platforms such as Azure, Databricks, and Spark, ensuring performance and scalability. Guide to best practices in leveraging these platforms to accelerate delivery.
- 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.
Requirements
- Bachelor's degree in computer science, Statistics, Engineering, Mathematics, or any quantitative field; master's or PhD is strongly preferred.
- Demonstrated success applying machine learning and statistical modeling techniques in production environments to drive measurable business impact.
- Experience mentoring and coaching data scientists, fostering skill development and knowledge sharing within cross-functional teams.
- Experience working with cloud platforms such as Azure, Databricks, and Spark for big data processing and analysis.
- Exceptional communication skills, with the ability to translate complex technical concepts into clear, actionable insights for both technical and non-technical stakeholders.
- Proficiency in Python/PySpark for data manipulation, analysis, and modeling tasks. Strong knowledge of relevant libraries and frameworks.
- Good knowledge of CI/CD tools like GitHub for version control and collaboration. Familiarity with other collaboration tools is a plus.
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