You're a data scientist with broad analytical and ML experience as well as production LLM expertise. You'll own the full spectrum of data science work at Qogita — from classical modelling and forecasting through to LLM-powered features — and act as the team's go-to on language model architecture, evaluation, and deployment. You'll take end-to-end ownership of complex ML systems and pipelines that are business-critical: designing them, shipping them, and keeping them healthy in production. The Data Science team works cross-functionally with Product, Engineering, and Commercial teams to build the intelligence layer that drives Qogita's marketplace.

• Build and deliver data science solutions across the stack — predictive models, ranking systems, demand forecasting, and LLM-powered features — depending on where the business need is greatest
• Take ownership of business-critical ML systems end-to-end: from problem framing and model design through to deployment, monitoring, and ongoing maintenance in production environments
• Act as the team's domain expert on LLMs: advise on model selection, architecture decisions, prompt engineering, fine-tuning, and evaluation
• Design and implement RAG architectures and evaluation frameworks where language models are the right tool for the problem
• Apply classical ML and statistical modelling to structured business problems — pricing signals, supplier matching, catalogue enrichment — with rigorous attention to measurement and validation
• Translate ambiguous business problems into tractable ML problems with clear success criteria, working closely with Product and Commercial stakeholders
• Collaborate with Engineers to ship models via reproducible MLOps workflows — experiment tracking, model serving, alerting, and production monitoring — with a high bar for reliability and observability
• Communicate model choices, limitations, and trade-offs clearly to non-technical stakeholders including Product and commercial leadership

• 3+ years working as a data scientist or applied ML engineer, with meaningful exposure across both classical ML and deep learning
• A track record of owning ML systems in production — not just building models, but maintaining, monitoring, and iterating on them as live business-critical infrastructure
• Demonstrable LLM expertise — hands-on experience building and evaluating LLM-powered systems in a production or near-production environment
• Solid grounding in ML fundamentals: statistics, probability, supervised and unsupervised learning
• Practical experience with transformer architectures and the major model families (GPT, Claude, Llama, Mistral), including RAG pipeline design and vector database usage
• Strong Python and SQL, with experience using LangChain, XGBoost, PyTorch, Hugging Face Transformers (or similar frameworks), MLOps tooling (experiment tracking, model serving, monitoring), and experience of orchestration for ETL pipelines (Airflow)
• Experience with cloud ML services on AWS, GCP, or Azure, including deploying and operating models in distributed environments
• Able to communicate uncertainty and model limitations clearly to both engineers and non-technical stakeholders