Head of Machine Learning Operations

• 
  
• Drive the overall MLOps strategy along with other members of the Data Science & Insights (DS&I) team, while also collaborating with senior leadership to align strategies with broader organizational goals and objectives.
  
• Lead the development of innovative software tools to service both our Data Science solutions and wider business operations using relevant cutting‑edge technologies (e.g. AWS, Git, Docker, Kubernetes, Jenkins)
  
• Ensure the architecture is continuously improved and evaluate emerging technologies and trends to maintain a competitive edge in the market
  
• Lead the development of tools/services that support critical operations such as release management, source code management, CI/CD pipelines, automation, serving ML models to production environments and many other key operations while also overseeing the integration of these solutions into our broader technology ecosystem.
  
• Champion ML model‑governance by establishing a full end‑to‑end lifecycle governance framework to ensure models are monitored, refreshed and performing at optimal levels over time.
  
• Collaborate closely with key stakeholders across various business functions, including Product & Technology (P&T), IT, and Developer Experience (DX) teams, to develop and prioritize a strategic Data Science DevOps roadmap that aligns with organizational objectives and drives innovation.
  
• Mentor and coach team members, providing guidance, support, and expertise on advanced MLOps practices, while also serving as a point of escalation for complex technical challenges and issues.
  
• Act as a strategic advisor to senior leadership, providing insights, recommendations, and strategic direction on Data Science MLOps initiatives, while also championing a culture of continuous learning, growth and innovation within the organization.
  

Reporting to: Director of Data Science & Insights

• 
  
• Working closely with other team leads across the business to prioritize your team’s work
  
• Liaising with other engineering colleagues across the business to ensure alignment across the organization
  
• Representing Data Science & Insights in engineering/technology discussions across the business
  
• Conducting research on Machine Learning, Engineering and DevOps to ensure our tech stack is continually improving and aligning with best practices
  

• 
  
• Identifying detailed requirements, sources, and structures to support solution development
  
• Determining the optimal solutions and technologies to use to solve the problem at hand
  
• Ensuring solutions are implemented with best engineering practices in mind (CI/CD, unit tests, integration tests, logging, monitoring, etc.)
  
• Developing scalable solutions that can be integrated into production environments if required
  
• Collaborating in the development and deployment of proposed solutions to a live environment and tracking the effects in real time
  

• 
  
• MVT – An in‑house built multivariate testing platform
  
• ACDC – Our solution for deploying ML to production
  
• Action Factory – An in‑house built automated decision‑making tool
  
• Echo – Our in‑house built MLOps pipeline tool
  
• Several in‑house built Python libraries
  

• 
  
• Effectively communicate outputs to other team members and the wider business in a concise manner that can be understood by both technical and non‑technical audiences
  
• Keep up to date with the latest techniques, technologies and trends and identify opportunities within the business where they could be applied
  
• Developing leading POCs to create breakthrough solutions, performing exploratory and targeted data analyses
  

• 
  
• M. xcfaprz S. or Ph.D. in a relevant technical field, or 5+ years’ experience in a relevant role.
  
• Solid understanding of DevOps practices or full‑stack software engineering in general
  
• Some experience of leading a team or keen interest in becoming a People Manager along with strong ability to coach high‑performing DevOps Engineers
  
• Expertise in writing production‑level Python code
  
• Expertise in cloud computing services like AWS, Google Cloud, etc.
  
• Expertise in Containerisation technologies like Docker, Kubernetes, etc.
  
• Expertise in software engineering practices: design pattern, data structure, object‑oriented programming, version control, QA, logging & monitoring, etc.
  
• Expertise in writing unit tests and developing integration tests to ensure quality of the product
  
• Experience and knowledge of Infrastructure as Code best practices
  
• Experience in developing GenAI tools seen as a plus point
  
• Knowledge of leading cross‑function projects and R&D projects
  
• Knowledge of agile project management
  
• Ability to communicate complex tools and technologies in a clear, precise and actionable manner, both verbally and in presentation format, to a broad variety of functional leaders