Data Scientist, Quince

• 5+ years of industry experience as a data scientist, with a strong preference for consumer-facing products or e-commerce applications operating at high scale
• MS or PhD in statistics, mathematics, engineering, computer science, or a highly quantitative field
• Exceptional proficiency in Python and SQL, alongside deep production knowledge of standard data science and machine learning libraries
• Hands-on experience developing and deploying production-grade Recommender Systems, Relevance Ranking, or Personalization pipelines
• Proven experience working with Generative AI or LLM application workflows for tasks like text parsing or context-driven query understanding
• Strong model productionalization skills with a customer-first mindset, allowing you to elegantly balance statistical accuracy against latency and engineering constraints in real-time environments

Desirable

• We are seeking a highly analytical and technical Data Scientist to join our Storefront team
• In this role, you will own end-to-end modeling work across high-priority surfaces — spanning Recommendations and Personalization, Search, and AI-powered Shopping Experiences — and your contributions will have direct, measurable impact on conversion, discovery, and user satisfaction at scale
• You will report to our VP of Engineering and collaborate closely with engineering managers, software engineers, and product managers to move our capabilities from traditional models into near real-time, personalized, and Generative AI-driven experiences
• Real-Time Personalization: Go beyond static product recommendations to develop and refine machine learning models that deliver near real-time personalization across our app and site surfaces, leveraging contextual signals to customize text and details for varied buyer intents (e.g., price-sensitive vs. quality/sustainability-first buyers)
• In-House Search Platform: Assist in building our natural language search engine from 0 to 1, utilizing Large Language Models (LLMs like Claude and GPT) for query understanding, semantic search, and advanced language retrieval
• App Engagement: Design and implement algorithms for interactive app experiences to drive daily customer engagement
• End-to-End Pipeline Ownership: Create new relevance ranking features, build automated end-to-end modeling pipelines in Python and SQL, deploy ML models into production, and run rigorous live experiments
• Data Infrastructure & Tooling: Identify data gaps, write clear data product specifications, and partner with Engineering to establish robust real-time tracking and performance monitoring frameworks
• Cross-Functional Partnership: Act as a technical partner to product and business squads, translating business targets into actionable data science solutions and presenting complex analytical outcomes to leadership