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
