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!

Would you like to meet the Team, see our office and much more? Visit our website:

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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