Intermediate Data Scientist - Bees Data

Join us to build the future of B2B commerce!
BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely. 
At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow. 
Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!  

• →Collaborate 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.

• Bachelor'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.