Data Scientist

Data Scientist II, Generative AIThis role owns generative AI and data science initiatives end to end within the environmental services space, from rapid prototyping through production deployment. The right person can move fluidly between experimenting with new AI ideas, building out an engineering focused waste classification project, and maintaining a live RAG system with multiple production APIs.Day to Day Responsibilities:Design, build, and deploy generative AI, RAG, and machine learning solutions that directly impact business outcomesRapidly prototype new AI use cases, including writing Python scripts that stand up vector databases and integrate LLMsTake LLM applications from concept through production deploymentPerform ongoing model changes, prompt updates, and end to end pipeline checksBuild and maintain an engineering project that codes and classifies waste profiles using customer and waste dataManage and maintain a RAG system supporting five production APIsPartner with a full stack development team on production deploymentsMonitor solution performance in production, resolving issues such as model drift, hallucination, and retrieval qualityTranslate ambiguous business problems into scoped, production ready analytical approachesCommunicate insights, tradeoffs, and recommendations to both technical and non technical stakeholdersDocument solution design, assumptions, and limitations to support reuse and transparencyMentor other data scientists and technical team members on modeling and architecture approachesRequirements:Must Haves:3 plus years of experience in a generative AI or applied AI focused role, not traditional data scienceStrong hands on RAG experience, including rapid prototyping, vector database setup, and LLM integrationAbility to clearly explain vector embeddings and cosine similarityExperience building LLM applications from concept to productionAWS experience, specifically deploying applications to EC2 or building CI/CD pipelinesAdvanced Python and SQL skills, including a strong understanding of window functionsBachelors degree in an analytical field such as Computer Science, Mathematics, Statistics, or EngineeringNice to Haves:Experience with prompt engineering and specific challenges faced applying itFamiliarity with API testing, git version control, and GitHub ActionsExperience monitoring AI application performance in productionFamiliarity with AI based coding assistants such as GitHub CoPilot, Cursor, or Claude CodeWorking knowledge of ML Ops tools and experiment tracking frameworksMasters degree