Role Purpose:
· 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 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
Key Duties & Responsibilities 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 organisationRepresenting Data Science & Insights in engineering/technology discussions across the businessConducting research on Machine Learning, Engineering and DevOps to ensure our tech stack is continually improving and aligning with best practicesLeading your team in developing industry leading MLOps solutions through:Identifying detailed requirements, sources, and structures to support solution developmentDetermining the optimal solutions and technologies to use to solve the problem at handEnsuring 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 requiredCollaborating in the development and deployment of proposed solutions to a live environment and tracking the effects in real timeManaging and maintaining existing DS tools/platforms/infrastructureMVT – An in-house built multi-variate testing platformACDC – Our solution for deploying ML to productionAction Factory – An in-house built automated decision-making toolEcho – Our in-house built MLOps pipeline toolSeveral in-house built Python librariesEffectively communicate outputs to other team members and the wider business in a concise manner that can be understood by both technical and non-technical audiencesKeep up to date with the latest techniques, technologies and trends and identify opportunities within the business where they could be appliedDeveloping leading POCs to create break through solutions, performing exploratory and targeted data analyses Knowledge and Key Skills: M.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 generalSome 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 codeExpertise in cloud computing service 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 productExperience and knowledge of Infrastructure as Code best practicesExperience in developing GenAI tools seen as a plus pointKnowledge of leading cross-function projects and R&D projectsKnowledge of agile project managementAbility 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
