Principal Data Scientist - Customer Experience

About the role

The Principal Data Scientist leads high-impact data science and AI initiatives that drive business growth, improve operational efficiency and enhance customer and associate experiences. A key focus is AI-powered customer support and agentic workflows that apply the reasoning and decision-making patterns of skilled associates so customers can resolve complex needs faster and with higher resolution quality. The role leads end-to-end AI and data science solutions, from experimentation and proof of concept through production deployment, and provides technical leadership and mentorship across data science teams.

Key responsibilities

• Solution development: design and build algorithms and models on large datasets, select and interpret advanced analytics methods, and communicate insights and recommendations to technical and non-technical leaders
• Project management and team support: set project goals, prioritize work, mentor junior colleagues, distribute workload, support recruiting and act as a technical subject matter expert
• Business collaboration: build trust with internal customers and cross-functional teams, educate them on advanced analytics, and seek new business opportunities
• Technical exploration: learn new developments, codify solutions, define best practices, share expertise at conferences and publish papers, and network with academic and industry experts

Required profile

• Age 18 or older and legally permitted to work in the United States
• Preferred: PhD or Master's degree in a quantitative field or equivalent practical experience
• Preferred: 10+ years of experience in data science, machine learning, AI or advanced analytics
• Preferred: Bachelor's degree or equivalent; no additional education or leadership experience required

Required skills

• Expert-level Python and modern data science and machine learning libraries
• Advanced SQL and large-scale cloud data platforms such as Google BigQuery
• Strong expertise in Generative AI and LLMs, including prompt engineering, RAG, embeddings and context management
• Experience designing agentic AI systems and multi-step workflows, and evaluating LLMs and agents
• Knowledge of model versioning, observability, experimentation and monitoring tooling