Data Scientist - Generative AI

Onsite in Phoenix, AZ – must currently permanently reside in Arizona. This position cannot offer relocation at this time and is not eligible for sponsorship. Must be authorized to work in the U.S.

About the role

This 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.

Responsibilities

• Design, build, and deploy generative AI, RAG, and machine learning solutions that directly impact business outcomes
• Rapidly prototype new AI use cases, including writing Python scripts that stand up vector databases and integrate LLMs
• Take LLM applications from concept through production deployment
• Perform ongoing model changes, prompt updates, and end-to-end pipeline checks
• Build and maintain an engineering project that codes and classifies waste profiles using customer and waste data
• Manage and maintain a RAG system supporting five production APIs
• Partner with a full-stack development team on production deployments
• Monitor solution performance in production, resolving issues such as model drift, hallucination, and retrieval quality
• Translate ambiguous business problems into scoped, production-ready analytical approaches
• Communicate insights, tradeoffs, and recommendations to both technical and non-technical stakeholders
• Document solution design, assumptions, and limitations to support reuse and transparency
• Mentor other data scientists and technical team members on modeling and architecture approaches

Requirements

• 3+ years of experience in a generative AI or applied AI-focused role, not traditional data science
• Strong hands-on RAG experience, including rapid prototyping, vector database setup, and LLM integration
• Ability to clearly explain vector embeddings and cosine similarity
• Experience building LLM applications from concept to production
• AWS experience, specifically deploying applications to EC2 or building CI/CD pipelines
• Advanced Python and SQL skills, including a strong understanding of window functions
• Bachelor’s degree in an analytical field such as Computer Science, Mathematics, Statistics, or Engineering

Preferred Qualifications

• Experience with prompt engineering and specific challenges faced applying it
• Familiarity with API testing, git version control, and GitHub Actions
• Experience monitoring AI application performance in production
• Familiarity with AI-based coding assistants such as GitHub CoPilot, Cursor, or Claude Code
• Working knowledge of ML Ops tools and experiment tracking frameworks
• Master’s degree

Benefits

• Health, dental, and vision coverage
• 401K retirement plan
• Paid time off (PTO)

Pay

$140,000–$150,000 per year