Head of ML Ops

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 full end-to‑end lifecycle governance framework to 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 Dev Ops 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 prioritise your team’s work.
• Liaising with other engineering colleagues across the business to ensure alignment across the organisation.
• Representing Data Science & Insights in engineering/technology discussions across the business.
• Conducting research on Machine Learning, Engineering and Dev Ops to ensure our tech stack is continually improving and aligning with best practices.
• Leading your team in developing industry leading MLOps solutions through:
• 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 practises 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.
• Managing and maintaining existing DS tools/platforms/infrastructure
• MVT – An in‑house built multi‑variate 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 break‑through solutions, performing exploratory and targeted data analyses.

• M.S. or Ph.D. in a relevant technical field, or 5+ years’ experience in a relevant role.
• Solid understanding of Dev Ops 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 Dev Ops Engineers.
• Expertise in writing production‑level Python code.
• Expertise in cloud computing service like AWS, Google Cloud, etc.
• Expertise in software engineering practices: design pattern, data…