Wonder is looking to solve complex operational challenges in their high-density restaurants and supply chain, ranging from food production, logistics, cooking and final delivery to customers.
Requirements
- Fluency in Python, SQL or similar scripting languages, knowledge of Java, Kotlin, C++, or other programming languages
- Deep understanding of core principles and state-of-the-art techniques in Operations Research and Data Science
- Experience with mathematical optimization frameworks such as CPLEX, Gurobi, Xpress, OR-tools
- Experience defining and implementing solutions for difficult problems that require consideration of relevant tradeoffs
- Experience in deploying OR solutions to production environments
- Hands-on experience using predictive modeling in optimization and simulation modeling contexts, specifically in operations and supply chain applications
- Experience with machine learning pipeline and data orchestration tools such as MLflow, Kubeflow Pipelines, Airflow, etc
Responsibilities
- Develop and implement models and algorithms* to solve core operational challenges in kitchen sequencing, order batching, and order release logic
- Design and execute "what-if" analyses and simulations* to evaluate the impact of proposed changes on key metrics like expo sit time, courier wait time, and kitchen throughput
- Conduct exploratory research* into optimization methodologies for our existing Kitchen Display System (KDS) simulator to achieve more globally optimal outputs
- Collaborate with engineering teams* to translate optimization models into production-level code and ensure seamless integration with our KDS
- Model and account for real-world uncertainty* in operational process times, leveraging probabilistic approachesto optimize kitchen operations
- Formulate multi-objective functions* to optimize for both our customers and business
- Advise on the evolution of the KDS simulator* to a stochastic modeling scheme and help incorporate forecasting for upcoming demand
Other
- 3-5+ years relevant experience and MS in one of the following disciplines: Computer Science, Data Science, Applied Math, Operations Research or a related quantitative field OR PhD
- Demonstrated ability to domain model complex problems and build solutions iteratively, starting with minimum viable techniques and adding complexity only as needed
- Strong communication and collaboration skills to work effectively with different stakeholders and cross-functional teams
- Experience working on successful applied research projects in industry environments
- Our hybrid model requires 3 days a week in the office.
