11 of 11 Domain Models Jobs in the UK excluding London

are keen to make one more hire to our growing team. You will be joining a high performing in-house Data Science team that specifically focusses on Marketing domain models. Working as one of our Senior Data Scientist at Saga, you will play a pivotal role in shaping our data strategy and driving the development of advanced analytics … learning platform Main Responsibilities Our Senior Data Scientist will be fully accountable for the following areas; Advanced Analytics & Modelling: Design, develop, and implement advanced machine learning algorithms and statistical models to solve complex business problems. Drive the exploration and adoption of cutting-edge data science techniques and technologies. Insights and Reporting: Extract actionable insights from large, complex datasets to … and non-technical counterparts. From start to finish of every project. Data Strategy and Model Governance: Collaborate with cross-functional teams to execute the company's data strategy. Ensure models are fair, explainable and in compliance with relevant regulations and policies. Project Management Manage and deliver projects on time, to high standards. Agile ways of working - Demonstrate experience in More ❯

developers Continuously develop your skillset via tailored professional development objectives and budget What we need from you: Significant experience building web-based applications using C# Demonstrable skills in domain modelling using Object Oriented techniques and principles Proficient in writing automated tests using frameworks such as NUnit, MSTest, or xUnit Strong knowledge of web application security and how to More ❯

and motivated Data Scientist to join our client. In this role, you will leverage your advanced analytical skills and programming expertise to extract insights from complex datasets, develop predictive models, and support decision-making for our diverse range of customers. As a mid-level contributor, you will work on a variety of data-driven projects, collaborate with cross-functional … teams, and help implement scalable solutions. Key Responsibilities Data Analysis & Modelling: - Analyze large, complex datasets to identify trends, patterns, and actionable insights. - Develop, implement, and optimize machine learning models to solve business problems. - Conduct A/B testing and experimental analysis to validate hypotheses. Data Management & Engineering: - Collaborate with data engineering teams to ensure data quality, accessibility, and efficiency. … Design and develop ETL pipelines and workflows for data preprocessing. - Develop automated tests to validate the processes and models you create. Collaboration & Communication: - Collaborate with stakeholders to define project goals, requirements, and deliverables. - Actively participate in design meetings to help shape the solutions that the team delivers - Present findings and recommendations to technical and non-technical audiences. - Acquire domain More ❯

ll Be Doing Leading the development and deployment of MBSE and MBE frameworks into live projects. Championing digital engineering innovations, including: Integration of AI into engineering toolsets Cross-domain modelling with mechanical, electrical, and software disciplines Engineering data modelling and analytics Delivering consultancy, training, and support across national and international project teams. Documenting best practices to shape guidance More ❯

You will also be required to regularly visit clients throughout the UK so must be comfortable with some travel. Responsibilities : Prepare for the analysis activity through reviewing the domain, evaluating stakeholders, determining the output required and how best to achieve success Conduct the analysis activity at customer locations in the UK or abroad to have face to face … Key Requirements: Excellent soft skills such as communication, presentation and negotiation Experience in delivering software using various software methodologies, particularly Agile and Waterfall Experience in creating UML, BPMN 2.0 models and diagrams, particularly conceptual domain models, activity diagrams, and use case diagrams. Experience in developing and documenting requirements, with acceptance tests, and providing supporting artefacts like More ❯

capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT). Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other … End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience More ❯

capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT). Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other … End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience More ❯

capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT). Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other … End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience More ❯

capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT). Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other … End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience More ❯

capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT). Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other … End AI Product Development : Work across the full ML lifecycle, from data collection and preprocessing to model evaluation, deployment, and monitoring. Full-Stack Integration : Develop APIs and integrate ML models into web applications using FastAPI, Flask, React, TypeScript, and Node.js. Vector Databases & Search : Implement embeddings and retrieval mechanisms using Pinecone, Weaviate, FAISS, Milvus, ChromaDB, or OpenSearch. Required skills & experience More ❯

work aligns with Bede's wider technical strategy and roadmap. Work with Solution Architects and Business Analysts to design thoughtful, scalable solutions. Apply robust architectural practices such as domain modelling to ensure our systems are clean, future-proof, and well-encapsulated. Execution Collaborate with team leads to finalise solution designs and guide teams through delivery. Provide hands-on More ❯