Design and govern the enterprise-wide scenario planning framework, including templates, taxonomies, and scalability standards. Build multi-layer simulation frameworks (deterministic, stochastic, Monte Carlo, empirical). Define relationships between scenario outputs and planning decisions (e.g., SL trade-offs, buffer logic, allocation rules, capacity constraints). Lead cross-functional scenario reviews with Finance, Category, Factory Ops, and Regional Planning. Identify and formalize structural drivers of risk: forecast drift, bias, lead-time volatility, cannibalization, velocity shifts, market shocks. Architect the technical foundation for the scenario engine (configs, abstraction layers, ML/optimization modules). Drive integration into IBP/S&OP cycles, including automated updates and governance. Mentor Expert and Specialist DSs; define capability roadmap for the scenario DSC (Data Science Center of Excellence). Represent DS in executive forums; simplify technical concepts for senior leadership. Ensure compliance with model governance, explainability, auditability, and risk controls. 10+ years in Data Science, Decision Science, Optimization, or Scenario/Risk modeling. Deep knowledge of scenario planning, stochastic methods, optimization theory, and forecasting analytics. Experience designing large-scale decision systems for planning (IBP, S&OP, supply/demand). Strong Python engineering + architectural design capability. Familiarity with Gurobi/OR-Tools, PyMC, Monte Carlo simulation engines, and time-series decomposition. Experience building frameworks, not just models; ability to define system-level abstractions. Excellent communication and executive influencing capability. Led scenario engines in global supply chains (consumer electronics, FMCG, automotive). Experience with agentic AI orchestration and LLM-assisted decision systems.