We have partnered with a fast-growing, product-led organisation that is investing heavily in modern data science and applied AI.
This is a chance to work on problems where experimentation, model impact and real-world deployment actually matter. The environment is hands-on, fast moving and built for people who want to push beyond dashboards and offline models.
This role is about building, testing and shipping intelligent systems. Think modern ML stacks, real-time data, large-scale experimentation and the intersection of classical data science with today's AI tooling.
About the role:
- Design and build end-to-end data science solutions, from problem framing to production deployment
- Work with large, messy, real-world datasets and turn them into models that influence product and business decisions
- Develop and iterate on machine learning models including predictive, probabilistic and optimisation-based approaches
- Experiment with modern techniques such as LLM-powered workflows, embeddings and retrieval-augmented approaches where relevant
- Partner closely with engineers and product teams to productionise models and measure real impact
- Own experimentation, validation, and monitoring to ensure models perform in live environments
About you:
- 4-6 years of experience in data science or applied machine learning roles
- Strong grounding in statistics, experimentation and model evaluation, not just model building
- Hands-on experience with Python and common data science libraries
- Comfortable working across the full lifecycle from exploration to deployment
- Experience working with modern ML tooling such as feature stores, model pipelines or real-time inference is a plus
- Curious by nature and excited by new approaches such as LLMs, agentic workflows and multimodal data
- Able to communicate complex ideas clearly to non-technical stakeholders
