Data Scientist

Role & responsibilities

Design, develop, and deploy machine learning and generative AI models to address business needs.

AgentOrchestrated EndtoEnd Planning: Enable autonomous, crossfunctional supply chain planning that synchronizes demand, supply, manufacturing, and fulfillment to customer outcomes.

Autonomous Performance & Loss Management: Deploy AI agents to continuously monitor service, cost, and fulfillment signals, proactively eradicating losses and improving plan adherence.

Intelligent Manufacturing & Capacity Optimization: Use agentic insights to analyze production constraints, expedite plans, optimize capacity utilization, and ensure on time production performance.

RealTime Disruption Response: Leverage AI to sense and respond to internal and external disruptions by dynamically rerouting logistics and rebalancing inventory to mitigate risk.

GoalDriven Inventory & Replenishment: Shift from rulebased planning to autonomous, goalbased replenishment driven by forecasted demand, sales trends, service levels, and costtoserve.

Agentic Procurement, Analytics & Data Foundation: Implement intelligent agents for sourcing and contract monitoring, enable agentassisted RCA and decision intelligence, and ensure highquality data integration across enterprise systems

Required Qualifications:

Experience with AI-driven supply chain platforms (e.g., Resilinc, Databricks, SAP Joule).

Strong understanding of end-to-end supply chain (S&P, Logistics, Fulfillment).

Experience in managing AI agents, machine learning, or autonomous systems.

Knowledge of Python, SQL, or RPA tools is a plus.