[L6-2]Staff Data Scientist (SCM Systems)

Company Introduction

Coupang is reimagining the commerce 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 a dynamice end-to-end retail and logistics network and a culture of customer centricity, Coupang has broken tradeoffs around speed, selection and price. Coupang wants to make you feel every day that “How did I ever live without Coupang?”.

Role Overview:

We seek an experienced Staff Data Scientist to drive innovation within our Supply Chain Management (SCM) technology team using advanced AI/ML and Operations Research. You'll primarily tackle challenges in inbound SCM (forecasting, inventory, purchasing) and Fulfillment Center operations, with opportunities across Marketing, Retail, and Last-Mile. Collaborating cross-functionally, you will rapidly design, build, and deploy production-ready solutions, playing a key role in shaping and executing our SCM automation strategy and directly impacting business outcomes

What You Will Do:

• Explore & Define Opportunities: Conduct Exploratory Data Analysis (EDA) using statistical techniques (e.g., histograms, boxplots, correlation analysis) and collaborate cross-functionally to translate business needs into well-defined data science problems with clear success metrics (e.g., precision, recall, RMSE, cost reduction).

• Design & Build Models: Develop and implement sophisticated models tailored to SCM challenges. This includes applying Machine Learning techniques (e.g., tree-based models like XGBoost/LightGBM, regression methods, deep learning like RNNs/LSTMs for forecasting) and/or Operations Research approaches (e.g., Mixed-Integer Programming (MIP), Linear Programming (LP), simulation) using tools like Python libraries (Scikit-learn, TensorFlow, PyTorch) and solvers (e.g., Gurobi, CPLEX).

• Prototype, Test & Validate: Build model prototypes, conduct rigorous offline validation and backtesting, and design/analyze online experiments (A/B tests) to prove the efficacy and business value of your proposed solutions before full-scale deployment.

• Deploy & Integrate: Work closely with ML Engineers and Software Engineers to deploy validated models into scalable, production-grade systems, ensuring proper integration with upstream data sources and downstream operational applications. Contribute to MLOps practices for robust deployment and maintenance.

• Monitor & Iterate: Establish automated monitoring dashboards and alerts for model performance and data drift. Analyze results, troubleshoot issues, and iteratively improve models based on ongoing performance and evolving business requirements.

• Communicate & Influence: Clearly document methodologies, present findings, and explain complex models to diverse audiences (technical and non-technical) to drive adoption and inform strategic decisions. Provide technical guidance and mentorship within the team.

Essential Qualifications

• Master’s degree or PhD in a quantitative field (e.g., Computer Science, Operations Research, Statistics, Engineering, Economics, Physics, Mathematics).

• + years of hands-on industry experience applying data science, ML, and/or OR techniques, including deploying models into production.

• Proven ability to independently scope, design, build, deploy, and monitor data science models/solutions.

• Strong programming proficiency in Python for data analysis (Pandas, NumPy), ML (Scikit-learn, TensorFlow/PyTorch/Keras), and experience with relevant OR solvers/libraries (e.g., Gurobi, CPLEX, PuLP, SciPy.optimize).

• Experience querying and manipulating large datasets using SQL and distributed computing frameworks (e.g., Spark, Dask).

• Understanding of core ML algorithms (trees, regressions, clustering, NNs), statistical modeling, optimization techniques (LP, MIP), and experimental design (A/B testing, causal inference basics).

• Excellent problem-solving, critical thinking, and communication skills.

Preferred Qualifications:

• PhD in a relevant quantitative field.

• Deeper theoretical understanding and practical expertise in core ML algorithms, statistical modeling, optimization techniques, and experimental design/causal inference.

• Experience specifically within Supply Chain Management (inbound forecasting, inventory optimization, purchasing automation, network design, FC operations), Logistics, or E-commerce.

• Demonstrated experience leading complex data science projects end-to-end.

• Deep expertise in specific areas like time-series forecasting (e.g., ARIMA, Prophet, DeepAR), inventory theory, large-scale optimization, reinforcement learning, or simulation.

• Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools/practices (e.g., MLflow, Kubeflow, model versioning, CI/CD).

• Proficiency in other programming languages relevant to data science or backend development (e.g., Java, Scala, Go).

• Experience mentoring junior data scientists or engineers.