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Salary: USD 150,000 - 200,000 / annual
Quince is a manufacturer-to-consumer (M2C) e-commerce brand delivering high-quality essentials at low prices across apparel, accessories, and home goods. The company operates at Series D stage with a mission to challenge traditional retail through direct relationships with manufacturing partners and proprietary technology and analytics.
You will join the Storefront team as a Senior Data Analyst, owning end-to-end analytics and data science support for cart, checkout, post-purchase (returns and exchanges), and accounts. This is a high-velocity environment with millions of customer sessions daily, real-time inventory and pricing signals, and decisions requiring both rigor and speed.
Key responsibilities include:
- Own analytics and data science for cart, checkout, post-purchase, and accounts from problem framing through statistical modeling to shipped recommendations
- Design and run experiments (A/B tests, holdouts) including sizing, guardrail metrics, variance reduction, and rigorous post-test readouts
- Apply causal inference methods (diff-in-diff, synthetic control, incrementality testing) when randomized testing is infeasible, particularly 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 to prioritize hypotheses, size opportunities, and translate findings into clear product requirements
- 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 SQL and Python pipelines for your own analyses; partner with Data Engineering on event instrumentation, validation, and data quality
- Adopt and champion AI/LLM-powered workflows to accelerate analysis and raise team throughput
- Communicate insights with clear, evidence-based storytelling to cross-functional stakeholders and leadership
You are described as a "super IC"—deeply technical, hands-on with Python and SQL daily, and fluent in statistics that make analysis trustworthy. You are excited by AI tools (LLM-assisted exploratory analysis, agentic coding, automated anomaly detection) and use them to move faster. You have grit to push through messy data, ambiguous asks, and legacy instrumentation. You thrive with autonomy in a fast-moving environment and set your own standards for rigor.
Requirements:
- Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Economics, Math, or related quantitative field
- 5+ years of experience as a data scientist or quantitative analyst, ideally on consumer-facing product, e-commerce, or marketplace teams
- Deep hands-on expertise in Python and SQL—you write and debug both daily, not just direct others
- Strong grounding in statistics and experimentation: hypothesis testing, regression, experiment design, and causal inference methods (diff-in-diff, propensity matching, incrementality/geo-lift testing)
- Track record of scoping and running experiments end-to-end from design through analysis to decision-ready readout
- Experience with BI/visualization tools (e.g., Looker, Mixpanel) and comfort building self-serve dashboards
- AI-forward: actively use LLMs and AI-assisted tooling in analytical and engineering workflow; stay current on emerging capabilities
- Excellent written and verbal communication; able to translate ambiguous cross-functional problems into clear analytical plans and recommendations
- Grit and ownership: comfortable with ambiguity, messy data, and fast-moving startup environment with limited process
