Role Summary
The Principal Data Scientist is the
technical and strategic owner of scenario planning across the organization. This role defines the global scenario architecture, ensures alignment with IBP and S&OP, leads multi-domain modeling (demand/supply/network), and drives the adoption of simulation, optimization, and agentic AI-based scenario engines. Acts as a senior advisor to Directors and VPs.
Key Responsibilities
- 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.
Required Skills & Experience
- 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.
Preferred Experience
- Led scenario engines in global supply chains (consumer electronics, FMCG, automotive).
- Experience with agentic AI orchestration and LLM-assisted decision systems.
