Data Scientist (Applied AI) - No C2C

Own end-to-end delivery of AI solutions from problem framing and exploratory analysis through production deployment and measurement. Design and deliver LLM-enabled analytics and \"Deep Research capabilities using RAG over structured and unstructured enterprise data. Build agentic workflows and multi-step orchestration (tool use, function calling, retrieval, and guardrails) to automate business processes. Develop and deploy advanced statistical and machine learning models supporting insurance, actuarial, claims, risk, and investment decision-making. Engineer features and context: build reusable feature pipelines, embeddings, vector search patterns, and semantic/metadata strategies. Define success criteria and evaluation plans: offline tests, human-in-the-loop review, and online measurement (A/B or phased rollout). Productionize and operate models: partner with engineers to implement CI/CD, monitoring, drift detection, prompt/version management, and incident response runbooks. Apply responsible AI practices consistently: bias and fairness assessment, transparency, documentation (modelcards), and audit-ready controls. Communicate insights and tradeoffs clearly to executives and technical teams (risk, compliance, security) and influence decisions with data. Contribute to reusable standards and patterns for MLOps/LLMOps across the enterprise (templates, libraries, and governance checklists).