About the company
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Data Scientist, Data Science based in United States.
Responsibilities
- Design, develop, and optimize Retrieval-Augmented Generation (RAG) solutions that combine large language models with enterprise knowledge stores, semantic layers, and operational data.
- Build and maintain AI knowledge architectures, including metadata frameworks, vector stores, business glossaries, semantic models, and contextual data repositories.
- 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.
- 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.
- Contribute to the modernization of analytics experiences by identifying opportunities to apply Generative AI and machine learning.
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.
Conditions
- Annual salary of $153,000–$180,000 in Massachusetts.
- Annual salary of $164,000–$192,000 in Downstate New York.
- Annual salary of $136,000–$160,000 in Upstate New York.
- Hybrid work environment with work-from-home flexibility.
- Multiple office-location options across New York and Massachusetts.
- Career progression opportunities.
- Opportunities to work on enterprise-scale Generative AI, machine learning, analytics, and data modernization initiatives.