Staff Data Scientist, Planning and Forecasting

ABOUT QUINCE

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.

OUR CULTURE

Quince is a culture built for builders by builders. Our way of working starts with a blank sheet of paper. We question conventional thinking, use technology and data to uncover new opportunities, and move quickly to turn ideas into reality. We aren’t interested in replicating how others do retail. We’re building a better way – at a speed and scale unlike anything that’s been done before.

We dream big and chase the hard problems others shy away from. Rejecting long-held assumptions is part of our company's DNA. Where conventional wisdom says you have to choose – soft or durable, speed or rigor, quality or price – we ask why that trade-off has to exist in the first place.

Our pace is fast and the bar is high because our customers expect a lot from us and we refuse to let them down. We believe the best results come from challenging ourselves, learning from one another, and building on each other's strengths.

Here, responsibility is not determined by role, tenure, or seniority. Every team member - no matter their level - has the opportunity to drive our business and shape our trajectory.

If you’re someone who likes to imagine new possibilities and build better systems rather than plug-in to outdated ones, Quince is the place for you.

THE ROLE

Staff Data Scientist, Planning and Forecasting

Quince is building its own supply chain planning science capability from scratch. This includes demand forecasting at multiple geographic scales, methodology-agnostic forecasting tournaments, inventory placement optimization across a growing international network, vendor performance modelling, and the raw material signal generation that links the forecast back to procurement before failures happen.

The Staff Data Scientist sets the science charter and writes the roadmap, driving its load-bearing components end-to-end. You’ll be the deep specialist on a broad mandate (the forecasting tournament implementation, the inventory placement model, the vendor performance system) with full ownership of the methodology, the production model, and the iteration loop.

You’ll work closely with charter leadership, mentor the DS3s and DS2s on the team, and partner directly with planning operators.

We expect AI-native science. The methodology you bring should already include LLM-aided exploratory work, agentic feature engineering, and AI-augmented experimentation. Your standards for what counts as a real result should be high enough that AI assistance accelerates rather than dilutes them.

The ideal candidate has roughly a decade of applied data science or operations research experience, with deep expertise in one or two domains relevant to supply chain planning. They’ve owned modelling workstreams end-to-end across multiple companies or products, and they have the craft and the patience to take a hard problem and stay with it until the model actually moves the metric.

They are excellent at being given an ambiguous problem and solving it exceptionally well. They mentor junior scientists; they earn trust with operators; they argue for the right methodology even when it’s the harder one to implement.

They are AI-native in their science workflow as a matter of course. They use LLMs in EDA and feature work, run agentic loops where they make sense, evaluate AI-driven models on equal footing in a tournament framework, and have the rigor to keep AI assistance from quietly degrading the science.

Responsibilities

Workstream Ownership

Own the science workstreams end-to-end: the forecasting tournament implementation, the inventory placement optimization, the vendor performance system, or the raw material signal pipeline - across methodology, production model, and iteration loop

Hold the methodological standard for your area: when to use which model class, what constitutes a defensible evaluation, what to do when the data is too sparse or too noisy

Partner with the Planning Tools engineering team on what your workstream needs from the platform, and on the constraints production places back on what you can build

Methodology & Rigor

Bring depth across statistical, ML, and AI-driven methods; evaluate them on their merits within a tournament framework rather than advocating any one school

Set the standard for experimentation discipline within the science team: clean splits, honest backtests, the willingness to reject your own hypothesis

Drive AI-native science workflow (LLM-aided EDA, agentic feature discovery, AI-augmented experiment design) with rigor to match

Mentorship

Mentor scientists within the team; raise the methodological floor of the people around you through code review, design discussion, and direct teaching

Business Partnership

Partner with planning operators on the problems within your workstream; translate their operational reality into well-defined modelling problems, and your model outputs into decisions they can act on

Hold the methodological line in business conversations: educate operators on what your models can and can’t support, and push back on misclassified signals or over-fitted requests

Qualifications

Required:

8+ years of applied data science or operations research experience, with deep expertise in one or two domains relevant to supply chain planning

Demonstrated ownership of modelling workstreams end-to-end; from problem framing through production deployment, iteration, and measured business impact

Real methodological breadth across forecasting and OR: classical, ML, and AI-driven approaches, with informed opinions about which to reach for

Engineering fluency to own your work end-to-end: feature pipelines, experiments, model serving

AI-native science practice you can speak to in detail with examples of where AI tooling materially changed how you do science, and where you held your standards against it

Track record of mentorship: scientists who became better because they worked with you

Preferred:

Advanced degree in a quantitative field (Statistics, CS, Operations Research, Engineering, Economics) preferred; PhD a plus

All posted ranges are reflective of base salary and may vary depending upon experience level and location. Bonus and equity may also be provided for eligible roles.

Pay Range

$250,000 — $285,000 USD

WHY QUINCE?

Joining Quince means being part of a mission-driven team reshaping retail. You will work alongside talented colleagues, tackle meaningful challenges, and contribute to building a more sustainable, accessible future for customers and partners alike.

EQUAL OPPORTUNITY & HIRING INTEGRITY

Quince provides equal employment opportunities to all employees and applications for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran or military status, sexual orientation, gender identity or expression, or any other characteristic prot