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Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformPythonPyTorchScikit-LearnSQL
About the role
Key responsibilities & impact- 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
- 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
Requirements
What you’ll need- 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
Benefits
Comp & perks- Holiday entitlement, 28 days with the option to buy/sell up to 5 days (note that 3 days are held for Christmas holidays)
- 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 schemes
- Gym & retail discounts
- Regular social events/activities
- A range of other benefits from our flexible benefits package
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningdata sciencePythonSQLLLM applicationsprompt engineeringRAG architecturesembeddingsvector searchstatistical techniques
Soft Skills
problem-solvingcommunicationcollaborationcuriosityleadershippragmatic mindsetenthusiasm for AIability to explain complex conceptsrelationship buildingknowledge sharing