Senior 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  If you’re someone who likes to imagine new possibilities and build better systems rather than plug-in to outdated ones, Quince is the place for you.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 Senior Data Scientist, Planning and Forecasting Quince is building its own supply chain planning science capability from scratch. This involves demand forecasting at multiple geographic scales, methodology-agnostic forecasting tournaments, inventory placement optimization across a growing international network, vendor performance modelling, raw material signal generation. The agenda is wide, the data is rich, and the business consequence is direct. As a senior data scientist on this team, you’ll own modelling workstreams within that agenda. You’ll work alongside the charter leadership setting the science roadmap and the engineering team building the platform around your work. This role comes with ample scope to do real, end-to-end science, and enough engineering support that models you build actually run. We expect AI-native science. Modern forecasting and OR work makes regular use of LLM-aided EDA, agentic feature engineering, and AI-augmented experimentation infrastructure; we want a scientist who already builds this way. The ideal candidate is a senior data scientist with 5-8 years of production experience, who has owned modelling workstreams from problem framing through deployment and iteration. They are opinionated about methodology, what it takes to design a defensible forecasting tournament, and how to handle demand patterns that don’t behave. They identify as a scientist but they build like an engineer when they need to. They can write the feature pipeline, run the experiment, and ship the model serving without waiting for a counterpart to translate. They earn the trust of business stakeholders through the clarity of their work, not the polish of their slides. They are AI-native in their science workflow, including LLM-aided exploratory analysis, agentic feature discovery, and AI-assisted experiment design, with the rigor to validate AI suggestions before they become decisions. They are at home in a tournament framework that includes statistical, ML, and AI-driven models on equal footing. Responsibilities Modelling Workstreams Own a meaningful slice of the science roadmap; for example, demand forecasting for a product category, vendor performance modelling, inventory placement optimization, or raw material signal generation. Run rigorous, well-documented experiments: data prep, feature engineering, model selection, evaluation, and the discipline of knowing when a result is real and when it isn’t Bring methodological breadth to bear: classical, ML, and AI-driven approaches evaluated on their merits within a tournament framework, not as advocacy for any one school AI-Native Science Use LLM-aided EDA, agentic feature discovery, and AI-augmented experiment design as default tools, applying high judgment to validate AI suggestions before they shape decisions. Contribute to the team’s standards for evaluating AI-driven forecasting and OR models alongside classical and ML approaches Production Discipline Ship models into production, not into reports. Own the deployment, the monitoring, and the iteration loop that keeps a model accurate as the world shifts under it Partner with the Planning Tools engineering team on what the platform needs to expose for your work (feature stores, evaluation harnesses, model registry) and what your work needs to be designed for in turn Business Partnership Work directly with planning operators on the problems they’re trying to solve; translate their operational reality into well-defined modelling problems, and translate your model outputs into decisions they can act on Hold honest opinions about what science can and can’t solve, and surface th