Lead Machine Learning Engineer

• About Diageo:

  Diageo is the world's leading premium drinks company with Outstanding collection of brands, such as Johnnie Walker, Smirnoff, Baileys, Captain Morgan, Tanqueray and Guinness. With over 200 brands in 180 countries and a global network of daring individuals, our teams blend a diverse range of experience, knowledge and skills. We connect customers and consumers to our iconic products and build innovative experiences that bring people together to celebrate life.

• Who we are:

  The Digital & Technology vision is to own the way in the transformation of Diageo business capabilities, demonstrating data & digital technology, to build a driven edge in the marketplace.

About the Function:
Our Digital and Technology (D&T) team are innovators, delivering ground-breaking solutions that will help craft the future of our iconic brands. Technology touches every part of our business, from the sourcing of sustainable ingredients to marketing and development of our online platforms. We utilise data insights to build competitive advantage, supporting our people to deliver value faster.

Our D&T team includes some of the most dedicated digital professionals in the industry. Every day, we come together to push boundaries and innovate, shaping the digital solutions of tomorrow. Whatever your passion, we'll help you become the best you can be, creating career-defining work and delivering breakthrough thinking.

• 6 to 8 years of hands-on experience working on ML engineering
• Experience in end-to-end model development life cycle spanning (but not limited to) data sampling, model training, deployment, and performance evaluation
• Expertise in Python with the ability to write performant production-quality code
• Familiarity with SQL, knowledge of spark and cloud data environments (Azure cloud services)
• Experience of using Azure DevOps, ADF, MLFlow, Feature Stores etc.
• Knowledge of building and deploying CI/CD pipelines
• Validated understanding of machine learning algorithms and techniques
• Experience working on large scale machine learning projects, handling big data, and distributed computing frameworks like Hadoop or Spark.
• Proficient in data manipulation, data preprocessing, and feature engineering. - Strong problem-solving and analytical skills.
• Experience of working in an Agile framework