Data Scientist

Our mission is to help large successful brands like Uber, Amazon, Wise, HelloFresh (and more!) put their customers at the centre of everything they do. Using best-in-class tech in a fast-developing AI space, our Customer Experience Intelligence platform continuously analyses explicit and implicit feedback to enable our clients to identify what they should do next.

We're hiring a Data Scientist to join the team and help build and ship the next generation of that stack.

👉 What you'll be doing:

• Train, evaluate, and iterate on ML models for customer feedback tasks, contributing to our custom fine-tuning pipelines and running experiments with rigour and clear documentation.

• Build and maintain LLM-powered features including retrieval pipelines, reranking systems, and insight generation — with support and guidance from senior team members.

• Contribute to evaluation frameworks: help build test sets, define metrics, and assess model quality across classification, extraction, and generative tasks.

• Work on semantic search and retrieval, developing a strong working understanding of embedding-based approaches and the methods that go beyond them.

• Write clean, well-tested code and collaborate with Engineering on model integration, data pipelines, and monitoring.

• Work with the wider Data Science team to translate business and product requirements into practical ML experiments and solutions.

• Stay close to relevant research and bring useful ideas from the literature into team discussions and experiments.

🧰 What you’ll need:

• A solid working knowledge of transformer architectures and how they are applied in NLP tasks.

• Proficiency in PyTorch, including training loops and standard model fine-tuning workflows; exposure to parameter-efficient techniques such as LoRA is a plus.

• Experience working with real-world text data across tasks such as classification, extraction, embeddings, or search — at a meaningful scale.

• Some exposure to instruction fine-tuning or model serving, with an interest in going deeper.

• A grounding in classical ML and statistics, and the instinct to reach for simpler methods when warranted.

• Familiarity with GenAI and agentic patterns, even if hands-on production experience is still developing.

• Clear communication skills and the ability to explain technical work to colleagues across functions.

• Genuine curiosity about AI and a habit of experimenting — you learn by doing.

• Good ownership instincts: you follow problems through rather than passing them on.

➕ It would be a bonus if you:

• Have an MSc in Computer Science, Machine Learning, AI, Data Science, Computational Linguistics, or a closely related STEM field.