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About The Position
Lead operations research and decision science initiatives to optimize supply chain planning and execution, focusing on demand–supply visibility, inventory positioning, constrained allocation, and capacity bottleneck mitigation. Partner with global stakeholders to translate complex planning needs into quantitative decision models, governed performance measures, and repeatable decision workflows aligned to execution reviews and S&OP cadences.
Requirements
- Education: Master’s degree in Computer Science (Data/Analytics), Operations Research, Industrial Engineering, Supply Chain Analytics, or a related quantitative field.
- At least 1 year of experience in the following:
- Leading operations research and decision analytics initiatives that translate planning needs into quantitative models and decision-ready recommendations.
- Developing models for inventory policy tradeoffs, constrained allocation, and capacity mitigation within ERP/MRP-driven environments.
- Building and governing model-ready datasets in cloud platforms (e.g., Snowflake/Databricks) using SQL, including data validation and quality controls.
- Designing and executing scenario analysis, sensitivity testing, and back-testing to validate model robustness and quantify uncertainty.
- Leading and mentoring analysts in model development, validation, and documentation standards.
Responsibilities
- Problem Formulation: Define complex decision problems by identifying business goals, decision variables, constraints, and objective functions for demand supply balancing, constrained resource allocation, and service-level protection.
- Model Development: Develop and implement analytical decision models for inventory prioritization and constrained part allocation using segmentation-based logic and optimization techniques, such as network flow and heuristic approaches where appropriate.
- Simulation & Analysis: Design scenario analysis frameworks using digital-twin style what-if simulation to evaluate tradeoffs under demand uncertainty and supply disruptions. Perform sensitivity analysis and stress testing to quantify impacts on service levels, inventory performance, and fulfillment risk.
- Data Foundations: Architect model-ready datasets in cloud data platforms, such as Snowflake, using SQL transformations. Define source-to-target mappings and implement automated data quality controls, including reconciliation, anomaly detection, and KPI validation.
- Operationalization: Automate recurring model runs and decision workflows using enterprise data and orchestration platforms. Monitor outputs and exceptions to ensure alignment with ERP/MRP planning signals and business cadence.
- Leadership: Provide technical leadership by mentoring analysts, establishing model governance and documentation standards, driving cross-functional adoption of model-driven workflows, and delivering executive-ready summaries to global stakeholder groups.
