You will set-up the machine learning capabilities within Diageo and lead a team of machine learning engineers
Youll apply machine learning techniques to a variety of modelling and relevance problems involving the data sciences team and stakeholders.
You will be part of the analytics engineering lifecycle, including designing distributed systems, writing production-level code for data sciences models, conduct code reviews while working alongside our data engineering and infrastructure teams.
Enable continuous integration / continuous deployment of models from data engineering pipeline through to production.
Collaborate with data engineers to develop data and model pipelines.
Implement machine learning algorithms and libraries.
Communicate complex processes to business leaders.
Top 3-5 Accountabilities
Co-ordinate the entire ML lifecycle (research, design, experimentation, development. deployment, monitoring, and maintenance)
You will work on cutting-edge problems with a view of action-orientated output not research
Responsible for rolling out ML Engineering (and DevOps as well) best practices & standards across the organization
Lead change management by creating action plans to move employees past barrier points and on to desired outcomes.
Recruit, manage, mentor, and inspire ML Engineers; managing performance, goals and development
Qualifications and Experience Required
Expertise in data engineering, data science & modeling
At least 12 years exp in Data Analytics domain
Strong distributed computing background with architecting & design experience
Experience in implementing CI / CD pipeline for complex solutions
Strong expertise in multi cloud platform building scalable, realiable & performance solutions (Azure, AWS & GCP)
Expertise in working with Python, Java, R and Scala
Solid expertise in big data technologies Spark framework / Databricks in a cloud environment
Experience with vision processing, deep neural networks, Gaussian processes, and reinforcement learning
A solid understanding of both probability and statistics
A firm understanding of mathematics (including the role of algorithm theory in machine learning and complex algorithms that are needed to help machines learn and communicate)
Experience using programming tools like MATLAB
Experience working with large amounts of data in a high throughput environment
Experience working with distributed systems tools like Etcd, zookeeper, and consul
Experience working with messaging tools like Kafka, RabbitMQ, and ZeroMQ
Extensive knowledge of machine learning evaluation metrics and best practices
Competency with infrastructure as code (for example, Terraform or CloudFormation)
Clear communicator who can easily translate complex information
Collaborative individual who thrives in a team environment, specifically within a matrix organization
Creative problem solver and innovator
Analytical, critical thinker
Leadership responsibilties and key stakeholders
Delivering performance Move effectively between strategy and operational detail and think and plan for the next quarter.
Create Possibilities Understand and execute on established product direction
Influencing across a matrixed organisation Utilising outstanding communication, facilitation and networking skills to build strong and valuable relationships
Creating Possibilities - Manage, mentor, and inspire agile teams; managing performance, goals and development
How will I succeed in this role
Focus on winning with customers and consumers.
Defining the right vision, roadmap and adoption plan for Machine learning.
Becoming a trusted partner of stakeholders
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