Q
Quince
Senior Data Analyst (Storefront)
Skills
SQL QueriesLookerData EngineeringE-commerce WebsitesPythonData ScienceData AnalysisAI
19 hours ago
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Important information
Contract type:
Permanent
Salary:
$150,000 – $200,000 / year
Location:
Palo Alto, CA, USA
Work mode:
Onsite
Published on:
22 October 2026
What they need
- 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
- 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- 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 have grit — you push through messy data, ambiguous asks, and legacy instrumentation to ship something correct, then iterate
- 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 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
- 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
- Experience with BI/visualization tools (e.g., Looker, Mixpanel) and comfort building self-serve dashboards that stakeholders actually use
- 5+ years of experience as a data scientist or quantitative analyst, ideally on a consumer-facing product, e-commerce, or marketplace team
- Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Economics, Math, or a related quantitative field
- Grit and ownership: comfortable with ambiguity, messy data, and a fast-moving startup environment with limited process
- Track record of scoping and running experiments end-to-end, from design through analysis to a decision-ready readout
- 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)
- Deep hands-on expertise in Python and SQL — you write and debug both yourself, daily, not just direct others to
- 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- Employment is contingent upon successful completion of a background check
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