Lead Data Scientist, Data Science

Accountabilities: • Design, develop, and optimize Retrieval-Augmented Generation (RAG) solutions that combine large language models with enterprise knowledge stores, semantic layers, and operational data to deliver accurate and business-relevant responses.

• Build and maintain AI knowledge architectures, including metadata frameworks, vector stores, business glossaries, semantic models, and contextual data repositories that help AI systems understand business data and processes. • Develop reusable AI skills, agents, copilots, and prompt frameworks that automate analytical workflows, KPI interpretation, root-cause analysis, dashboard generation, and insight discovery. • Lead AI cost-optimization initiatives through effective use of retrieval patterns, knowledge stores, caching, model selection, and token-management strategies while maintaining solution performance. • Partner with engineering teams to integrate and deploy AI capabilities across business applications, dashboards, web-based solutions, and enterprise analytics environments. • Apply best practices for prompt engineering, grounding, hallucination mitigation, retrieval quality, and AI evaluation to improve the reliability and effectiveness of AI-powered solutions. • Contribute to the modernization of analytics experiences by identifying opportunities to apply Generative AI and machine learning to customer and operational use cases.

Requirements

• Bachelor’s, Master’s, or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or another related quantitative discipline. • Strong Python development skills, with demonstrated experience creating production-ready analytics, AI, automation, API integration, or data engineering solutions. • Hands-on experience building and deploying Retrieval-Augmented Generation (RAG) solutions and working with vector databases, embeddings, semantic search, and document retrieval technologies. • Experience working with enterprise AI technologies, including LLM APIs, prompt engineering, retrieval frameworks, AI assistants, copilots, or AI-driven workflow automation. • Experience integrating large language models through platforms such as OpenAI, Snowflake Cortex, Databricks AI, Anthropic, or comparable technologies. • Strong SQL and modern data-platform expertise, including data modeling, transformation, optimization, and working with cloud environments such as Snowflake, Databricks, Microsoft Fabric, or equivalent platforms. • Knowledge of machine learning, statistical analysis, predictive modeling, knowledge architectures, and AI evaluation frameworks is highly desirable. • Experience deploying AI and analytics solutions through APIs, web applications, dashboards, or enterprise reporting platforms is preferred. • Strong analytical, problem-solving, collaboration, and communication skills, with the ability to work effectively across technical and business teams.