Senior Data Analyst, Storefront

Palo Alto · United States - Palo Alto · USD 150,000-200,000 · 5+ years experience · Posted Oct 9, 2026

At a glance

Quince is a destination for builders, creators, innovators, and operators who want to come together and challenge the status quo. Our mission is simple: make really high quality essentials for really low prices, fairly and sustainably. We deliver on that mission through a unique manufacturer-to-consumer (M2C) model eliminating the layers of traditional retail that add cost and result in consumers paying more than they need to. We find, build relationships with, and work directly with the manufacturing partners behind some of the world’s finest products. From there, our teams design smart, efficient operational processes and build and deploy proprietary technology, AI, and analytics to help us scale fast.
What began with a small assortment of elevated basics has quickly grown into a cross-category brand spanning apparel, accessories, home goods, and more. Today, tens of millions of people across a growing number of countries come – and return – to Quince because they trust us to deliver.

Quince is seeking a Senior Data Analyst to join the Storefront team, partnering across cart, checkout, post-purchase (returns & exchanges), and accounts to quantify what is working, what is broken, and what to build next.
This role sits at the center of a live, high-velocity storefront managing millions of customer sessions, real-time inventory and pricing signals, and decisions requiring rigor and speed.
The individual will build statistical models, design and run a disciplined experimentation program, and partner directly with Product, Engineering, and Design.

Own end-to-end analytics and data science support for cart, checkout, post-purchase (returns & exchanges), and accounts from problem framing to statistical modeling to shipped recommendation.
Design and run experiments (A/B tests, holdouts) across surfaces, including sizing, guardrail metrics, variance reduction, and rigorous post-test readouts.
Apply causal inference methods (e.g., diff-in-diff, synthetic control, incrementality testing) when randomized testing isn't feasible.
Drive funnel diagnostics and root-cause analysis across cart abandonment, checkout drop-off, return/exchange friction, and account engagement.
Partner directly with Product, Engineering, and Design to prioritize hypotheses, size opportunities, and translate findings into clear product requirements and trade-offs.
Build and maintain source-of-truth metrics and self-serve dashboards for cart, checkout, post-purchase, and accounts KPIs, flagging regressions and anomalies proactively.
Write and maintain SQL and Python pipelines behind analyses and partner with Data Engineering on event instrumentation, validation, and data quality.
Adopt and champion AI/LLM-powered workflows from analysis acceleration to automated anomaly narratives to raise the team's analytical throughput.
Communicate insights with clear, evidence-based storytelling to cross-functional stakeholders and leadership; run post-mortems and document learnings.

Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Economics, Math, or a related quantitative field.
5+ years of experience as a data scientist or quantitative analyst, ideally on a consumer-facing product, e-commerce, or marketplace team.
Deep hands-on expertise in Python and SQL.
Strong grounding in statistics and experimentation: hypothesis testing, regression, experiment design, and causal inference methods.
Track record of scoping and running experiments end-to-end, from design through analysis to a decision-ready readout.
Experience with BI/visualization tools (e.g., Looker, Mixpanel) and comfort building self-serve dashboards.
AI-forward with active use of LLMs and AI-assisted tooling in analytical and engineering workflows.
Excellent written and verbal communication skills to translate ambiguous, cross-functional problems into clear analytical plans and recommendations.
Grit and ownership, with comfort handling ambiguity, messy data, and a fast-moving startup environment with limited process.