Data Scientist 2/3, Storefront

Quince•Palo Alto, CA

2d•$171,000 - $242,000

About The Position

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.

Requirements

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.

Nice To Haves

Experience replacing or augmenting traditional NLP systems with LLMs for complex text parsing and language understanding.
Familiarity with Business Intelligence tools (e.g., Looker, Tableau) to build internal dashboards for model tracking and metrics visibility.
Experience building data product specifications alongside distributed engineering teams to streamline model evaluation

Responsibilities

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.