Senior Data Analyst, Storefront

Quince•Palo Alto, CA

14d•$150,000 - $200,000

About The Position

We're seeking a Senior Data Analyst to join our Storefront team, partnering across cart, checkout, post-purchase (returns & exchanges), and accounts to quantify what's working, what's broken, and what to build next. This role sits at the center of a live, high-velocity storefront: millions of customer sessions, real-time inventory and pricing signals, and decisions that need rigor and speed in equal measure. You'll build statistical models (causal inference, incrementality testing, funnel diagnostics) that separate signal from noise, design and run a disciplined experimentation program, and partner directly with Product, Engineering, and Design to prioritize the highest-impact opportunities across the post-cart customer journey. You are a true individual-contributor “super IC” — deeply technical, hands-on with Python and SQL every day, and fluent in the statistics that make an analysis trustworthy rather than just directional. You're excited by the newest AI tools and use them to move faster: LLM-assisted exploratory analysis, agentic coding for pipeline work, automated anomaly detection and insight generation. You have grit — you push through messy data, ambiguous asks, and legacy instrumentation to ship something correct, then iterate. You thrive with autonomy in a fast-moving environment where you set your own standards for rigor and communicate findings with clarity and confidence to stakeholders at every level.

Requirements

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 — you write and debug both yourself, daily, not just direct others to.
Strong grounding in statistics and experimentation: hypothesis testing, regression, experiment design, and causal inference methods (e.g., diff-in-diff, propensity matching, incrementality/geo-lift testing).
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 that stakeholders actually use.
AI-forward: you actively use LLMs and AI-assisted tooling in your own analytical and engineering workflow, and you stay current on what's newly possible.
Excellent written and verbal communication; able to translate ambiguous, cross-functional problems (cart, checkout, returns, accounts) into a clear analytical plan and a clear recommendation.
Grit and ownership: comfortable with ambiguity, messy data, and a fast-moving startup environment with limited process.

Responsibilities

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 these 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, especially for returns/exchanges policy and account-experience changes.
Drive funnel diagnostics and root-cause analysis across cart abandonment, checkout drop-off, return/exchange friction, and account engagement — segmented by device, traffic source, geo, and cohort.
Partner directly with Product, Engineering, and Design on these teams 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/accounts KPIs; flag regressions and anomalies proactively.
Write and maintain the SQL and Python pipelines behind your own analyses; 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 so they compound.