3 of 3 Remote/Hybrid Data Modelling Jobs in Oxford

ideal candidate will enjoy being involved in a wide variety of projects, combining technical and consultancy skills. Typical project areas include: • Requirements capture & data modelling exercises • Bespoke software projects • Deployment, customisation & configuration of enterprise deployment of our clients products • Advisory services to assist clients with decision-making with … respect to application of technology, software development and data modelling Important to success in this role are: • Technical ability and an enthusiasm for technical challenges • An ability to communicate technical information in a client-friendly way • An understanding of the importance of process • A tendency to seek opportunities ...

enhance the performance, stability, and sensitivity of next-generation technologies. Working closely with experimental scientists and hardware engineers, you will translate complex physical system data into actionable improvements through intelligent data modelling and automation. Key Responsibilities Develop and implement custom machine learning models for signal processing, sensor … fusion, and system optimisation Design models for applications such as noise suppression, drift compensation, anomaly detection, and adaptive calibration Build and maintain robust data pipelines to process high-dimensional experimental and time-series datasets Train, validate, and optimise machine learning models using modern Python-based frameworks Integrate ML inference ...

core platform infrastructure is being built alongside the game itself. You will be making foundational choices about stack architecture, service boundaries, deployment pipelines, and data models that the team builds on for years to come. This is not a maintenance role. You are building systems from the ground up. … Kubernetes on Azure for container orchestration and deployment. Establish deployment practices that support rapid iteration without compromising stability. Make architectural decisions about service boundaries, data models, caching strategies, and API design that balance immediate delivery needs with long-term scalability. Establish monitoring, alerting, and incident response practices using Grafana ...