Senior Data Analyst, Quince
Storefront
$150-200k
Office located in Palo Alto, CA
Affordable luxury fashion & home goods
Be an early applicant
Affordable luxury fashion & home goods
1001+ employees
Be an early applicant
$150-200k
Office located in Palo Alto, CA
1001+ employees
Company mission
To create products of equal or greater quality than the leading luxury brands at a much lower price.
Role
Who you are
- 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
- 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
What the job involves
- 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
Application process
- Employment is contingent upon successful completion of a background check
Our take
Recently, there has been more widespread awareness of the alarming waste of resources and environmental impact caused by the fast fashion industry. Recognizing these concerns, Quince is looking to offer a more sustainable alternative to consumers.
Quince is a direct-to-consumer brand that offers high-quality products at lower prices by cutting out the middlemen. The company provides wardrobe staples and home goods, with an emphasis on minimalism, simplicity and timeless designs.
The company works directly with factories to produce inventory on a near-just-in-time basis. By eliminating unnecessary intermediaries, Quince can streamline the supply chain and reduce financial and environmental waste.
This approach allows Quince to ship goods directly to consumers' doorsteps, providing a more efficient and sustainable shopping experience. This has proven very popular, allowing the company to raise significant funding, reach a valuation of $4.5 billion and expand to Canada.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
58% employee growth in 12 months
Company
Funding (last 2 of 7 rounds)
Mar 2026
$500m
SERIES E
Jul 2025
$200m
LATE VC
Total funding: $958m
Company HQ
SoMa, San Francisco, CA
Leadership
Sid Gupta
(CEO & Co-founder)Graduated from Stanford University with an MBA in Finance. Previously the Founder and CEO of Lolli and Pops as well as the Director of Strategy at TechTurn.
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