Data Scientist

Description -

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.

Job -

Data & Information Technology:

Schedule -

Full time

Shift -

First Shift (India)

Travel -Relocation -Equal Opportunity Employer (EEO) -

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