About the role Delivers machine learning and modern AI (including generative AI where relevant) solutions end-to-end, from problem framing to production deployment, partnering closely with product, data engineering and business teams. Responsibilities • Translate business needs into ML/AI problem statements and measurable success metrics. • Develop ML models and, where relevant, GenAI components (e.g., retrieval-augmented generation, prompt pipelines) with clear evaluation criteria. • Run evaluation: offline metrics, error analysis, bias checks, and monitoring baselines; document decisions and assumptions. • Communicate results and limitations clearly to technical and non-technical stakeholders; support adoption in workflows. Technical requirements • Experience: 4–6+ years delivering end-to-end data science projects. • Education: Master’s or PhD in Data Science, Machine Learning, Statistics, Computer Science, Applied Mathematics, or related quantitative field (required from this level onward). • Core stack: Python, Spark, Git; ML frameworks; Databricks/MLflow (or equivalent); cloud basics. Preferred skills Technology->AI-Data science->AI-Data science,Technology->AI-Generative AI->Generative AI - Basic,Technology->Cloud Data Platforms->Azure Analytics Services->Azure Databricks Education Bachelor of Engineering