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.