Description

What we would like you to bring to the role….key experience, knowledge, skills & personal qualities

• Developing and maintaining ML pipelines for training, testing, deployment, and monitoring of machine learning models. 
• Transitioning models from development to production in collaboration with Lead Data Scientists. 
• Providing technical support to analysts, including enabling tool access, data integration, and workflows. 
• Building and maintaining tools for model tracking, versioning, and experiment management. 
• Integrating CI/CD practices into the ML lifecycle for faster and more reliable deployments. 
• Implementing monitoring solutions for model performance and data drift. 
• Optimizing infrastructure and workflows for scalability, cost-efficiency, and performance. 
• Supporting troubleshooting and incident response for ML services in production. 
• Managing APIs and data connections, including integration with analytics tools (e.g., Tableau). 
• Implementing and managing orchestration solutions for ML workflows. 
• Utilizing AWS Lambda and other serverless technologies for scalable, event-driven processes. 
• Collaborating across teams, applying strong communication, problem-solving, and stakeholder management skills. 
• Adapting to changing priorities, managing multiple projects, and maintaining detail under pressure. 
• Working in broadcast, production, start-up/scale-up, or media/marketing/advertising contexts. 
• Operationalizing ML models in commercial, direct-to-consumer organizations. 
• Collaborating with agencies and external technical partners on deployment or integration projects. 
• Applying agile methodologies and product development practices, including continuous delivery of ML and analytics capabilities. 

• Best practices for ML pipeline design, deployment, and maintenance. 
• Model tracking, versioning, and experiment management tools and techniques. 
• CI/CD principles and tools relevant to ML workflows. 
• Monitoring approaches for ML model performance and detecting data drift. 
• Infrastructure optimization strategies for scalability, cost control, and performance enhancement. 
• API management and integration approaches for analytics platforms (e.g., Tableau). 
• Orchestration tools and solutions for automating ML workflows. 
• AWS Lambda and serverless architectures for ML applications. 
• Communication, collaboration, and stakeholder engagement best practices in technical environments. 
• AdTech and MarTech ecosystems, including Customer Data Platforms and Data Clean Rooms. 
• Data infrastructure and deployment considerations in broadcast, streaming, and advertising industries. 

• Bachelor’s degree in computer science, software engineering, data engineering, or related field; master’s degree preferred OR similar knowledge gained through relevant experience. 
• Ability to design solutions to loosely defined operational and deployment challenges, ensuring ML models can be reliably delivered, scaled, and maintained in production. 
• Proficiency in programming with Python, including experience with ML frameworks and libraries (e.g., scikit-learn, TensorFlow, PyTorch). 
• Strong understanding of CI/CD practices, containerization (e.g., Docker), orchestration tools (e.g., Kubernetes, Airflow), and cloud platforms (e.g., AWS, SageMaker). 
• Experience integrating and automating workflows across large, varied datasets, and architecting specialized database and computing environments for ML workloads. 
• Skilled at simplifying complex technical problems into clear, actionable components and resolving them systematically. 
• Able to effectively delegate, manage your own time, and priorities work across multiple concurrent projects and environments. 
• Confident making operational decisions in unprecedented situations, and able to provide informed recommendations with authority and clarity. 
• Capable of developing strong relationships with senior stakeholders, analysts, and data scientists, and adept at collaborating across departments. 
• Communicates effectively using both technical and non-technical language, adapting to stakeholders at all levels. 
• Strong attention to detail, critical thinking skills, and the ability to work under pressure in production environments. 
• Demonstrates excellent problem-solving, incident management, and team motivation skills. 

What you will get to work on….key outputs and responsibilities of the role

• Design, develop, and maintain production-ready ML pipelines to support targeted advertising, personalized recommendations, and other AI-driven features on the U app. 
• Operationalize propensity models, audience segmentation models, and other predictive assets developed by the Data Science team, ensuring seamless deployment and ongoing performance monitoring. 
• Implement robust monitoring, alerting, and retraining strategies to maintain model accuracy, detect data drift, and minimize downtime. 
• Build and manage APIs, orchestration processes, and integrations with analytical tools (Tableau) to ensure analysts and stakeholders can easily access model outputs and insights. 
• Optimize ML infrastructure for scalability, cost-efficiency, and performance using AWS services (including SageMaker, Lambda, and containerized deployments). 
• Support ad hoc operational requests, including deploying proof-of-concept ML projects into production environments. 
• Collaborate with Data Scientists and Analysts, to improve data quality, metadata management, and data pipelines that feed ML models. 
• Stay up to date with industry trends and best practices in MLOps. 
• On occasion, lead or contribute to pan-BBC MLOps projects, collaborating with the BBC Studios Data Science team and those maintaining the BBC Studios Data Platform to share learnings and ensure platform consistency. 
• Provide technical guidance and mentoring to team members working on ML deployment and operational tasks. 

About UKTV