models, from problem framing and experimentation through to live deployment and monitoring. Technical depth: Apply strong statistical modelling, ML, and (where relevant) NLP/LLM techniques to solve real business problems, not just exploratory analysis. Experimentation and measurement: Design and run experiments (A/B testing, causal inference) to validate … SQL. MLOps: MLflow, cloud ML platforms (Azure ML, AWS SageMaker, or Databricks). Focus areas: Predictive modelling, recommender systems/personalisation, and increasingly, applied LLM/GenAI use cases, reflecting where the market is heading right now. Infrastructure: Cloud based (AWS/GCP/Azure, client dependent). What ...