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.