As an Applied GenAI Data Scientist, you will work at the heart of bringing GenAI into real business processes, ensuring solutions move beyond pilots and are robustly embedded where they truly make a difference. You will contribute to initiatives that range from early design and experimentation to strengthening, evaluating, and integrating existing GenAI applications so they deliver reliable and sustainable value.
Working closely with engineers, product teams, and stakeholders, you will focus on making GenAI work in practice. From agent evaluation and prompt optimization to guardrails, LLMOps, and golden source data, your work will help ensure AI solutions are trustworthy, scalable in use, and built to support long term impact.
Roles and responsibilities:
- Own GenAI solutions from early experimentation through production, ensuring they are robustly embedded in business processes and deliver sustainable impact
- Lead the transition from GenAI pilots to production ready solutions that create measurable business value
- Develop and improve GenAI applications, including agents and LLM integrations, with a focus on reliability, performance, and long term usability
- Evaluate and monitor GenAI behaviour in production, including prompt performance, agent quality, and ongoing model validation
- Embed GenAI solutions into existing business processes to ensure adoption, trust, and real world impact
- Ensure GenAI applications meet risk, compliance, and governance requirements, including guardrails and responsible AI practices
- Advise and influence technical and non technical stakeholders on the effective and responsible use of GenAI to maximise impact
Requirements:
- MSc or PhD in a quantitative field such as Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or equivalent practical experience
- 4 or more years of relevant hands on experience in the field of AI, with a broad and in depth understanding of core algorithms and methods
- Strong experience writing clean, readable, well documented and efficient Python code suitable for production environments
- Proven hands on experience with GenAI systems, including agent frameworks for example Google ADK, prompting, RAG, evaluation, LLMOps, and building golden source data
- Solid data analysis skills using SQL and tools such as PySpark, BigQuery, or similar, combined with the ability to tell a clear story with data to explain findings and convince stakeholders
- Experience collaborating effectively in cross functional teams, working in an Agile Scrum environment, and openness to feedback and continuous growth