Digitization & Analytics Manager - Digitization & Analytics Manager - Strategic Sourcing
Responsibilities & Key Deliverables
Key Responsibilities
AI & ML Strategy for Strategic Sourcing
Partner closely with Purchasing, Commodity Teams, Supplier Quality, Cost Engineering, and Strategic Sourcing stakeholders to identify AI/ML use cases and business transformation opportunities.
Understand and map procurement processes including:
RFQ management
Supplier evaluation
Spend analytics
Cost optimization
Contract analytics
Demand forecasting
Supplier risk monitoring
Procurement operations automation
Define AI roadmap aligned with organizational digital transformation goals.
Build scalable AI frameworks and reusable AI components for sourcing functions.
AI/ML Solution Development
Design, develop, and deploy AI/ML applications for procurement and sourcing functions.
Develop predictive, prescriptive, and generative AI solutions using structured and unstructured enterprise data.
Build intelligent copilots and AI assistants for procurement users using LLM technologies.
Implement:
NLP models
Recommendation systems
Forecasting models
Supplier intelligence engines
Semantic search systems
Document intelligence solutions
Generative AI workflows
Create AI-enabled dashboards and analytical insights platforms.
Large Language Model (LLM) & GenAI Responsibilities
Architect and implement enterprise-grade LLM applications.
Fine-tune and optimize LLMs for procurement-specific use cases.
Build Retrieval-Augmented Generation (RAG) frameworks using enterprise data sources.
Integrate AI copilots with sourcing workflows and enterprise systems.
Develop prompt engineering frameworks and AI governance standards.
Evaluate open-source and commercial GenAI ecosystems for enterprise adoption.
Ensure responsible AI, security, compliance, and data governance.
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Enterprise Systems Integration
Lead integration of AI applications with enterprise platforms including:
SAP S/4HANA
Qlik SaaS
3DEXPERIENCE (3DX)
PLM systems
Supplier portals
Internal analytics platforms
Develop API-based architectures and middleware integrations.
Collaborate with IT, Digital, Data Engineering, and Cloud teams for enterprise deployment.
Ensure scalability, cybersecurity, reliability, and performance optimization.
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Full Stack & Application Development
Lead development of AI-powered web applications and user interfaces.
Build scalable front-end solutions using React.
Develop backend AI services and APIs using Python.
Implement cloud-native and microservices-based architectures.
Drive DevOps and MLOps best practices for continuous deployment and monitoring.
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Stakeholder & Team Management
Work cross-functionally with sourcing leaders, business users, digital teams, and external partners.
Conduct workshops to identify automation and AI opportunities.
Mentor data scientists, AI engineers, and analysts.
Drive vendor evaluations and technology partnerships.
Preferred Industries
Education Qualification
Bachelor's or Master's degree in: Computer Science, Artificial Intelligence, Data Science, Information Technology, Electronics Engineering or related field
Preferred:MBA or specialization in Supply Chain / Analytics / AI
General Experience
8-15 years of experience in AI/ML, analytics, or enterprise digital transformation.
Strong experience in manufacturing, automotive, procurement, or supply chain environments preferred.
Proven track record of delivering enterprise AI applications.
Critical Experience
Technical Skills
Core AI & ML
Expert in Python programming
Strong expertise in:
Machine Learning
Deep Learning
NLP
Generative AI
LLM architectures
Hands-on experience with:
LangChain
Vector databases
RAG pipelines
Prompt engineering
AI orchestration frameworks
Experience with ML frameworks:
TensorFlow
PyTorch
Scikit-learn
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Enterprise Systems
Strong knowledge of:
SAP S/4HANA
Qlik SaaS
3DEXPERIENCE (3DX)
Enterprise data lakes
API integrations
Middleware systems
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Development Skills
Strong React development knowledge
REST API and microservices architecture expertise
Experience with:
FastAPI / Flask
Docker
Kubernetes
CI/CD pipelines
MLOps platforms
Cloud platforms (Azure/AWS/GCP)
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Data & Analytics
SQL and enterprise data modeling expertise
Experience handling large-scale enterprise datasets
Dashboarding and analytical platform integration experience
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Preferred Industry Knowledge
Automotive purchasing and sourcing processes
BOM and cost structure understanding
Supplier ecosystem management
Manufacturing digital transformation
Procurement analytics and spend management
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Behavioral Competencies
Strong problem-solving and analytical mindset
Excellent stakeholder management
Strong communication and presentation skills
Ability to convert business problems into AI-driven solutions
Innovation-oriented with execution focus
Ability to work in fast-paced cross-functional environments
