As a Senior Analytics Engineer, you will lead high-value client engagements from discovery

to solution architecture. You will translate supply chain challenges—such as demand

volatility, inventory imbalances, and logistics bottlenecks—into technical AI & Analytics

implementation roadmaps that deliver measurable financial impact.

Key Responsibilities

 Strategic Assessment: Evaluate client supply chain ecosystems, data maturity, and

operational friction points to build high-impact AI use cases.

 Solution Architecture: Architect scalable AI/ML blueprints for demand forecasting,

dynamic pricing, inventory optimization, network routing, and predictive maintenance.

 Proof of Concept (POC): Lead the creation of POCs and functional prototypes to

validate data feasibility and demonstrate ROI to executive leadership.

 Cross-Functional Orchestration: Bridge the gap between enterprise supply chain

teams and data engineers to build, deploy, and monitor production-ready models.

 Technical Presales: Deliver tailored solution walkthroughs, technical product

demonstrations, and business case proposals to stakeholders.

 AI Governance: Ensure supply chain automation workflows adhere to data security

standards, ethical AI frameworks, and corporate risk policies.

Required Qualifications

 Education: Bachelor’s or Master's degree in Supply Chain Management, Operations

Research, Data Science, Computer Science, or an equivalent technical field.

 Core Experience: 10+ years of professional experience, with a proven track record

in software solutions consulting, enterprise architecture, or digital transformation

within supply chain operations.

 Domain Expertise: Deep knowledge of core supply chain functions including

demand/supply planning, warehousing (WMS), SIOP, transport management (TMS),

sales & operations planning (S&OP), and ERP environments (like SAP or Oracle).

 Advanced Analytics & Data Fluency: Strong conceptual and hands-on familiarity

with the modern AI landscape, including predictive modelling, optimization

algorithms, multi-agent frameworks, Large Language Models (LLMs), and Retrieval-

Augmented Generation (RAG).

 Cloud & Tools: Experience working alongside cloud infrastructures (AWS, Azure, or

GCP) and understanding how data pipelines feed machine learning workflows.

 Soft Skills: Outstanding executive presentation skills, a consultative mindset, and

the ability to influence cross-functional business and technical teams.