Senior Data Scientist position in the BEES team focusing on AI strategy at AB InBev. The role involves designing, developing, and deploying advanced machine learning and deep learning solutions for portfolio optimization, recommendations, personalization, promos, segmentation, imputation, and insight discovery. Responsibilities include collaborating with cross‑functional teams, mentoring junior and mid‑level data scientists, optimizing large‑scale data processing workflows on cloud platforms, and ensuring robust, scalable solutions aligned with business objectives.
Senior Data Scientist | BEES Personalization at AB InBev
Remote - Remote
Requirements
Education
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or any quantitative field
- Master’s or PhD strongly preferred
Experience
- Demonstrated success applying machine learning and statistical modeling techniques in production environments to drive measurable business impact
- Experience mentoring and coaching data scientists and engineers
- Experience working with cloud platforms such as Azure, Databricks, and Spark for big data processing and analysis
Skills
- Machine learning and deep learning algorithms
- Python/PySpark programming
- CI/CD tools like GitHub for version control
- Workflow and automation tools
Responsibilities
- 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 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.
- 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.
Technologies
AzureDatabricksSparkPythonPySparkGitHubCI/CD toolscollaboration tools
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