Senior Director, Supply Chain Management Autonomation
Coupang is reimagining the shopping experience with the goal of wowing each customer from the instant they open the Coupang app to the moment an order is delivered to their door.
Powered by an outstanding end-to-end e-commerce and logistics network and a fanatical culture of customer centricity, Coupang has broken tradeoffs around speed, selection and price. Today, we provide exceedingly fast shipping speeds on millions of items including fresh groceries, delivered within hours nationwide, 365 days a year.
We are doing this for millions of consumers in Korea. Korea is home to one of the largest and fastest growing e-commerce opportunities anywhere in the world.
Supply Chain Management Autonomation (SCMA) is responsible for Demand Forecasting, Automated Ordering and Inventory Management for Coupang’s Rocket delivery network. SCMA ensures that customer demand is accurately predicted and that Coupang carries sufficient inventory to meet customer availability goals for millions of products. SCMA builds models and systems to break the tradeoff between inventory and availability, constantly pushing the efficient frontier closer to the ideal. Our forecasting models use a variety of traditional Time Series and ML / Deep Learning models to predict customer demand for every product, every day. And our inventory ordering and management models ensure that Coupang provides the best buying experience for our customers, while minimize excess inventory and waste.
As leader of the SCMA Data Science organization, you will drive a team of Data Scientists to solve key challenges in inventory management: improving availability while reducing inventory. You will provide thought leadership on Supply Chain Optimization, including assortment planning, multi-echelon inventory optimization and evaluation of inventory policy effectiveness.
As the leader of Coupang SCM Autonomation Optimization and Simulation team you will be responsible for optimizing Coupang’s supply chain performance. Main responsibilities include:
- Apply state-of-art Operations Research methodology to solve business and operation challenges in Supply Chain
- Design and implement ordering models and inventory management models to achieve optimal inventory health
- Advise senior leadership on relevant research impacting our inventory management processes
- Lead simulation projects and experiments to provide decision-making support in Supply Chain planning
- Collaborate with engineering teams to implement algorithms/models into production environments and evaluate outcomes
- Collaborate with product owners and stakeholders to identify opportunities to improve Supply Chain performance
- (preferred) Ph.D. in Operations Research, Industrial Engineering, Statistics, Applied Mathematics or a related field and 10 years relevant work experience
- (optionally) Master’s degree from those fields and at least 15 years relevant work experience
- Hands-on experience designing and implementing optimization algorithms (e.g. linear programming, integer programming, non-linear programming) and (meta-)heuristics (e.g. genetic algorithms, PSO) to solve practical problems in supply chain at e-commerce scale
- Proven record in converting high-level business requirements into mathematical models and simulation models (e.g. Discrete Event Simulation models, Agent-Based Simulation models)
- Working experience in supply chain management (e.g. inventory management, ordering systems, capacity planning)
- Proficiency with relevant programming languages (e.g. Python, Java, Scala) to implement the model accompanied by expertise in the use of associated optimization tools (e.g. Cplex, OR-Tools, Gurobi) and simulation engines (e.g. AnyLogic, Arena, Simio)
- Excellent data query (SQL) and visualization skills (e.g. Tableau, PowerBI) with analytical and statistical mind to manipulate data under big data environment
- Good communication and presentation skills in English with both technical and business stakeholders
- Familiarity with relevant techniques in Machine Learning (Deep Learning, Reinforcement Learning) is a plus