CHAPTER DATA SCIENTIST | BEES PERSONALIZATION

Chapter Data Scientist | BEES Personalization - 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 and the companies that service them, unlocking new growth opportunities for all. 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, promos, segmentation, imputation, and insights discovery. The ideal candidate should hold a bachelor's degree in any quantitative field; strong mastery of frontier algorithms in machine and deep learning; plus working knowledge of cloud platforms (Azure, Databricks) for ML applications. Proficiency in Python/PySpark programming, collaboration tools, and working knowledge of CI/CD tools like GitHub is required. Knowledge of workflows for automation is also required. What you will doCollaborate with team members to analyze complex datasets, develop and implement cutting-edge machine learning and deep learning algorithms for portfolio optimization, recommendations, personalization, promos, segmentation, imputation, and insights discovery. Stay up to date with the latest advancements in machine learning and deep learning algorithms, ensuring a strong technical foundation to solve complex business problems. Apply frontier algorithms to drive innovation across the team's projects. Utilize cloud platforms like Azure, Databricks, and Spark to efficiently process and analyze large-scale datasets. Leverage the full potential of these platforms to deliver scalable and performant solutions. Utilize Python/PySpark for data manipulation, analysis, and modeling tasks. Implement automation workflows to streamline processes and enhance productivity using tools and frameworks. Collaborate closely with cross-functional teams, including data engineers, business stakeholders, and product managers, to understand project requirements and deliver high-quality solutions. Embrace an agile development approach to iterate quickly and efficiently. QualificationsBachelor's degree in computer science, engineering, mathematics, or any quantitative field. A master's degree is a plus. Strong technical skills and deep understanding of frontier algorithms in machine learning and deep learning. Familiarity with techniques for portfolio optimization, recommendations, personalization, promos, segmentation, imputation, and insights discovery. Proficiency in machine learning frameworks such as TensorFlow or PyTorch. Experience in implementing and deploying models using these frameworks. Experience working with cloud platforms such as Azure, Databricks, and Spark for big data processing and analysis. 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. Understanding of workflows and automation tools to streamline processes and enhance efficiency. Availability to work in the hybrid model in Campinas or São Paulo. What We Offer:Performance based bonus* Attendance Bonus* Casual office and dress code Days off* Health, dental, and life insurance Discounts on Ambev products* School materials assurance Language and training platforms 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 race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics. #J-18808-Ljbffr