Senior Data Scientist

We are hiring a Senior Data Scientist - Generative AI to design, develop, and productionise advanced analytical, machine learning, and Generative AI solutions that create measurable business value. You will work across the full data science life cycle, from opportunity discovery and experimentation through to model evaluation, deployment, monitoring, and continuous improvement. This is a senior individual contributor role with technical leadership scope: you will shape data science approaches, mentor colleagues, lead design reviews, and make practical trade-offs across classical machine learning, statistical modelling, NLP, LLM-based applications, Retrieval-Augmented Generation, evaluation frameworks, and MLOps.

Senior Data Scientist with a specialism in Generative AI for an exciting, dynamic role within the AI and Data Team. Work alongside an eager team where collaboration, innovation, and personal development are key pillars. You will apply advanced analytics, machine learning, natural language processing, and Generative AI techniques using modern platforms such as Databricks, Microsoft Fabric, and Azure AI services.

Duties & Responsibilities:

• Lead the design, development, and deployment of data science, machine learning, and Generative AI solutions that address priority business use cases.
• Develop predictive, classification, forecasting, optimisation, and anomaly detection models using robust statistical and machine learning methods.
• Design and implement Generative AI solutions, including Large Language Model applications, Retrieval-Augmented Generation pipelines, semantic search, summarisation, classification, question answering, and content generation.
• Define model evaluation approaches for accuracy, relevance, reliability, bias, safety, hallucination risk, cost, and performance.
• Collaborate with data engineers, analytics engineers, product owners, and business stakeholders to translate ambiguous problems into practical analytical and AI solutions.
• Apply MLOps and software engineering best practices, including version control, testing, CI/CD, monitoring, documentation, and reusable components.
• Strong Python and SQL skills, with experience using libraries such as pandas, NumPy, scikit-learn, and relevant statistical or machine learning packages.
• Hands-on experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, model selection, and model validation.
• Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking, and evaluation.
• Experience with NLP techniques such as text classification, entity extraction, semantic similarity, summarisation and information retrieval.
• Experience using Databricks, Spark, and lakehouse concepts for large-scale data preparation, experimentation, and model development.
• Familiarity with Microsoft Fabric, Azure AI services, Azure Machine Learning, or comparable cloud-based AI and data science platforms.
• Experience with MLOps practices, including Git, automated testing, CI/CD, model registry, deployment, monitoring, and experiment tracking.
• Ability to use AI-assisted development tools responsibly to improve productivity, code quality, and documentation.
• Proven track record delivering data science or AI solutions into production or near-production environments for BI, analytics, automation, or decision support use cases.

Certifications (Nice to Have):

• MSc or PhD in Data Science, Artificial Intelligence, Computer Science, Statistics, Mathematics, Engineering or equivalent practical experience.
• Relevant Microsoft Azure certifications or exams such as AI-900, DP-900, DP-100, DP-203 or AI-102.
• Relevant Databricks certifications, such as Machine Learning Associate, Machine Learning Professional or Data Engineer Associate.
• Any recognised Generative AI, LLM, NLP, Responsible AI or MLOps certifications are advantageous.

Personal skills:

• Ability to analyse complex, ambiguous business problems and translate them into practical data science and AI approaches.
• Outstanding verbal and written communication abilities.
• Outstanding interpersonal skills.
• Self-Starter.
• Strong conceptual abilities.
• Excellent multitasking abilities.
• Exceptional analytical abilities.
• Quickly learn and evaluate emerging AI, Generative AI and data science tools, methods and concepts.
• High attention to detail.
• Pragmatic judgement around responsible AI, data privacy, security, governance and ethical model use.