Data Scientist focusing on applied AI and machine learning solutions for clients. Collaborating with teams to design, build, and deploy advanced analytical capabilities.
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 LangChain.
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 DevOps 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, LangChain/Graph/Smith, Vector databases, Graph Structures)
Support experimentation and prototyping, helping move promising ideas into production-ready solutions
Work closely 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
Requirements
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 work independently 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
Benefits
Holiday entitlement, 28 days with the option to buy/sell up to 5 days
Day off (on or in the week of) your birthday
Pension eligibility, up to 5% matched contributions
Private healthcare
Life assurance
Enhanced maternity and enhanced paternity and shared parental leave
Cycle to work & electric car schemes
Gym & retail discounts
Regular social events/activities
A range of other benefits from our flexible benefits package
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