2 of 2 DemandTrendContract Senior Data Scientist Jobs in the UK excluding London

Lead Data Scientist - GenAI Specialist (Contract Role) We are seeking an experienced Lead Data Scientist with expertise in Generative AI (GenAI) to take ownership of end-to-end data science solutions for high-impact projects across a range of industries. This is a contract position offering the opportunity to lead cutting-edge AI initiatives, collaborate … with diverse teams, and deliver tangible business value through advanced analytics and machine learning. About the Role As the Lead Data Scientist , you will: Drive the design and delivery of end-to-end data science solutions with a focus on GenAI and Large Language Models (LLMs) . Manage and mentor junior data scientists and analysts, ensuring … best practices and high delivery standards. Collaborate with cross-functional teams to integrate AI solutions into business processes. Present compelling insights and recommendations to senior stakeholders and clients. Contribute to project proposals, solution design, and business development initiatives. This is an ideal role for someone who thrives on solving complex problems with innovative approaches while balancing real-world practicality More ❯

Senior Data Scientists – London – Hybrid Job description BDO Regulatory Solutions are currently recruiting for a Senior Data Scientists to join our client, a regulated firm, based in London.We are offering an initial 6 month contract starting as soon as possible with a competitive rate, employed via an Umbrella company. About the role: We are looking for … talented and experienced data scientists with experience to join our programme. Solid knowledge and experience of AI and ML is essential. Key responsibilities include: Design and develop AI/ML based solutions Work with other data scientists to build and deploy production-level solutions Troubleshoot and debug code Work with other teams to understand and solve business problems … About you: Python (pandas, NumPy, scikit-learn): For data wrangling, modelling, and feature engineering SQL: For querying structured data sources Model Development & Validation: Experience with classification, unsupervised learning (e.g. outlier detection), and ranking models Machine Learning Deployment: Familiarity with containerised deployment (e.g. Podman, SageMaker, DSW pipelines) Version Control (Git): To maintain reproducible and collaborative workflows Time-Series Analysis More ❯