We are seeking a (Senior) Data Scientist who operates as a full-stack, well-rounded tech expert, capable of working across the entire value chain from data ingestion and modelling to advanced machine learning, GenAI, and Agentic AI. You will contribute directly to TK Elevator’s global Common Data Platform (CDP) and support high-impact initiatives such as CDP Nexus, Programmatic AI, and the AI Foundation. This role requires someone who understands the evolution from rule-based systems to statistical ML and now to AI/LLMs and Agentic AI, including the challenges in reliability, context, security, and data architecture that each paradigm brings. Key Responsibilities Advanced Analytics, Machine Learning & AI Develop, optimize, and deploy ML models (regression, classification, clustering, time series, NLP). Build GenAI and Agentic AI prototypes and production-grade components, leveraging LLMs, RAG, vector search, and agent orchestration (e.G., Databricks Agent Bricks). Understand and navigate the transition from rule-based analytical systems → classical ML → LLM/GenAI architectures, including their implications for data, context, governance, and reliability. Conduct feature engineering and modelling using PySpark, MLflow, and Databricks workflows. Data Exploration & Insights Explore IoT, operational, service, sales, and financial datasets to generate actionable insights. Collaborate with analysts to refine metrics, KPIs, and Power BI dashboards. Collaboration & Platform Thinking Work closely with Data Engineers to prepare reliable Silver/Gold datasets for ML and AI workloads. Align models and AI components with the CDP Nexus semantic model and shared data structures. Contribute to reusable ML/AI artifacts, shared features, and platform components. Model Productionisation & DataOps Collaboration Deploy and maintain models in production using Databricks pipelines, Delta tables, Unity Catalog, and CI/CD flows. Collaborate with DataOps to ensure model reliability, testing, data quality, and monitoring. Document assumptions, modelling logic, and dependencies according to TKE standards. AI / GenAI / Agentic AI Enablement Build and test LLM applications, agentic workflows, retrieval pipelines, embedding models, and vector stores. Contribute reusable AGI components for the AI Foundation and Programmatic AI stream. Help define guardrails for safe, contextual, and reliable AI. #J-18808-Ljbffr