Supply Chain Analytics Manager Full Time

About The Position

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.