Machine Learning Engineer

Digital & Technology Team (D&T) is an integral division of HEINEKEN Global Shared Services Center. We are committed to making Heineken the most connected brewery. That includes digitalizing and integrating our processes, ensuring best-in-class technology, and embedding a data-driven culture. By joining us you will work in one of the most dynamic and innovative teams and have a direct impact on building the future of Heineken!

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Your Responsibilities Would Include

• Developing production-level ML libraries and algorithms for existing and future products
• Writing code to enhance machine learning capabilities
• Reviewing code developed by other team members, providing feedback and insights
• Infrastructure Development and Management
• Developing and improving the infrastructure used for building and deploying machine learning models
• Improving pipelines for data ingestion and feature engineering
• Designing and implementing processes for end-to-end management of machine learning pipelines
• Building data processing pipelines, large-scale machine learning systems, and big data technologies (e.g., Hadoop, Spark)
• Developing and implementing scalable and efficient model training workflows on a large scale
• Demonstrating very good coding skills and software development experience
• Understanding fundamental data science concepts and have experience with common tooling and packages used for machine learning
• Applying professional software engineering and best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
• Writing clean, efficient, documented, and scalable code.
  
  

You Are a Good Candidate If You Have

• At least 3 years of experience applying Machine Learning in the industry
• Experience in writing production code for machine learning models
• Experience related to using ML infrastructure at scale
• Very good coding skills and software development experience
• Fluency in Python programming
• Working knowledge of Databricks and PySpark
• End-to-end hands-on experience with building data processing pipelines, large-scale machine learning systems, and big data technologies (e.g., Hadoop, Spark)
• Fluency in extracting information from databases and good SQL skills
• Understanding of fundamental data science concepts and experience with common tooling and packages used for machine learning
• Knowledge of application architectures and design patterns.
  
  

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