Quince is looking to optimize its inventory and logistics planning through data science solutions to improve inventory levels and in-stock rates across all categories, ultimately influencing the future success of the company.
Requirements
- 7+ years of experience as a data scientist in relevant industry, with hands-on experience applying machine learning, predictive modeling, optimizations, and GenAI to optimizing inventory and logistics planning.
- Deep knowledge in: Statistical, optimization and machine learning techniques.
- Deep knowledge in: Data science libraries in a programming or scripting language (proficiency with Python and SQL)
- Deep knowledge in: Model productionalization
- Experience with BI platforms such as Looker, Tableau, etc.
Responsibilities
- Develop and execute the data science strategy for inventory and logistics planning, aligning with broader business goals.
- Design, develop, and refine predictive models for demand forecasting, inventory optimization, and workforce allocation.
- Leverage advanced machine learning and optimization techniques to solve complex supply chain and planning problems at scale.
- Deploy the ML models into production, run experiments, and enable performance monitoring in production.
- Enhance data infrastructure: Identify gaps in existing data, create data product specifications, and collaborate with Engineering teams to implement enhanced data tracking.
- Drive automation: Continuously strive for automated and production-ready solutions to improve efficiency and scalability.
Other
- Candidates based in the SF Bay Area will be required to work in a hybrid capacity out of our Palo Alto office, 3 days/week (Monday, Wednesday and Thursday).
- All candidates outside of that region will be able to work in a fully remote capacity.
- No relocation is required.
- Excellent communication and presentation skills.
- Move fast, be a team player, and be kind.
