Staff Data Scientist, Quince

• 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

Desirable

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