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 organisation
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
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
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 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 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 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
