Note: The job is a remote job and is open to candidates in USA. Quince is a tech company disrupting the retail industry by leveraging AI, analytics, and automation to create high-quality products at low prices. The Staff Data Scientist will own the science workstreams related to supply chain planning, including demand forecasting and inventory optimization, while mentoring junior scientists and ensuring methodological rigor in their work.
Responsibilities
- 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
Skills
- 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
- Advanced degree in a quantitative field (Statistics, CS, Operations Research, Engineering, Economics) preferred; PhD a plus
Benefits
- Bonus and equity may also be provided for eligible roles.
Company Overview
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