Senior Data Scientist

Responsibilities

• Design, build and deploy machine learning models to solve real-world business problems, including classification and optimisation use cases
• Develop and implement LLM-based applications, including prompt engineering, fine-tuning where appropriate, and orchestration of model/agent workflows via tools like Lang Chain.
• Build and maintain RAG, GRAPH pipelines, including document ingestion, embedding generation, vector search and retrieval strategies
• Evaluate model performance and trade-offs, balancing accuracy, explainability, cost and scalability
• Use Python as the primary language for data science and ML development.
• Write, optimise, and maintain SQL queries against relational databases to support analytics, feature generation, and model development.
• Collaborate on data pipelines and feature engineering to support model development and deployment
• Apply statistical and analytical techniques to inform insights and actions from the data.
• Work with structured and unstructured data, including text-heavy datasets used in LLM and RAG/GRAPH solutions
• Contribute to model deployment approaches with our Dev Ops team, including APIs, batch processes and integration with existing analytics platforms
• Work with cloud-based platforms and services (e.g. AWS, Azure, GCP) to support model training, deployment and scaling
• Use and evaluate modern AI tooling, frameworks and libraries (e.g. PyTorch, scikit-learn, Lang Chain/Graph/Smith, Vector databases, Graph Structures)
• Support experimentation and prototyping, helping move promising ideas into production-ready solutions
• Establish successful working relationships with business stakeholders and Domain leads to translate business problems into data science and AI solutions
• Partner with the Lead Data Scientist to identify new AI-driven opportunities and help shape Transform’s AI capability and offerings
• Clearly communicate complex technical concepts, assumptions and outputs to non-technical audiences
• Document approaches, models and learnings to support knowledge sharing and reuse

• Strong hands-on experience in data science and machine learning, with evidence of delivering production or near-production solutions
• Solid experience building models and applying statistical techniques using Python (experience with R is desirable but not essential)
• Practical experience with LLMs, including prompt engineering and building LLM-enabled applications
• Experience designing or working with RAG architectures, embeddings and vector search
• Strong understanding of machine learning fundamentals, including model evaluation, bias, overfitting and explainability
• Experience working with cloud services for data science and AI workloads
• Familiarity with MLOps or model deployment practices is desirable (e.g. versioning, monitoring, reproducibility)
• Strong problem-solving skills and a pragmatic mindset. Focused on delivering value, not just experimentation
• Ability to lead data science projects while collaborating effectively in multidisciplinary teams
• Excellent communication skills, with the ability to explain complex concepts simply
• Curiosity and enthusiasm for emerging AI technologies, with a desire to continuously learn and experiment