Supply Chain Data Scientist 3

Knowledge/Skillset

• Strong foundation in data science and forecasting, including time-series models (ARIMA, Prophet, XGBoost and other ML-based forecasting approaches).
• Experience applying machine learning techniques to real-world business problems, particularly in demand forecasting or predictive analytics.
• Proficiency in Python, SQL, Excel (PowerPivot), and PowerPoint, using them to build, test, visualize, and present optimization and analytical models.
• Understand modern data ecosystems, including relational databases, data‑warehouse design, cloud storage, and ETL best practices supporting optimization and ML workflows.
• Experience with GitHub and cloud compute/ML platforms, such as AWS SageMaker, Microsoft Azure, Google Cloud, Kubeflow, or Databricks.
• Excel in project management and cross‑functional collaboration, ensuring smooth delivery of optimization and analytics initiatives.
• You take initiative and drive Optimization and ML projects end‑to‑end: from problem formulation, data acquisition, and stakeholder alignment to modeling, solution refinement, productionization, and performance monitoring.

Preferred Requirements

• 5+ years of experience in data science, forecasting, or predictive analytics, ideally in Supply Chain, Telecom, Retail, or CPG.
• Master’s degree in data science, statistics, computer science, or a related field.
• Experience with large-scale forecasting systems and demand planning processes.
• Familiarity with feature engineering for time-series and hierarchical forecasting techniques.
• Experience with cloud-based ML platforms (Databricks, SageMaker, Azure ML, etc.).
• Knowledge of demand planning, forecasting processes, and related metrics (forecast accuracy, bias, demand attainment)
• Proven track record of delivering measurable improvements in forecast accuracy or business KPIs.
• Flexibility to collaborate with US based teams and support critical work aligned to U.S. time zones when needed.

Employees at all levels are expected to

Education