Lead Data Scientist, Data Science

United StatesRemoteFull-timelead

Data ScientistData

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Key Responsibilities

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Data ScientistData

The Lead Data Scientist will drive the development of next-generation AI-enabled analytics solutions across a large customer organization. This role combines Generative AI, machine learning, semantic modeling, and modern enterprise data platforms to transform how teams access and use insights. You will design scalable AI solutions using operational data, knowledge stores, call transcripts, and semantic layers to support better business decisions. The position offers substantial hands-on ownership across RAG architectures, AI agents, copilots, and intelligent analytical workflows. You will collaborate closely with operations, technology, engineering, and transformation teams to bring AI capabilities into real-world business applications. The role is ideal for a technically strong data scientist who enjoys translating emerging AI technologies into measurable enterprise value.

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

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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.