Senior Data Scientist (Supply Chain Management) - Data Labs (m/f/d)

Anyone can build an AI agent. What makes SAP's agents different is accuracy grounded in the richest enterprise data and process context in the world. As a Data and Applied Scientist at SAP, you'll build the context engine grounded in SAP’s Business ontology: the semantic infrastructure that transforms raw business data into the knowledge layer powering SAP's AI agents and assistants.

The semantic and contextual foundation of SAP's AI. While generic AI agents operate on surface-level patterns, SAP agents are accurate because they understand the real semantics of enterprise business master data, process flows, and domain relationships. You'll build and scale the layer that makes that possible.

• Design and maintain enterprise ontologies and semantic models that give AI agents accurate, grounded understanding of SAP and connected business landscapes harmonizing data from SAP, Salesforce, Workday, ServiceNow, MES/IoT systems, and external providers into unified semantic layers.

• Build AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding that make SAP's agents accurate and reliable in production.

• Develop AI capabilities including generative AI and LLM-based solutions using enterprise business data, knowledge graphs, business process intelligence, and other structured and unstructured data assets.

• Leverage SAP's deep data and process context including SAP data models, metadata structures, and business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce to ground AI solutions in real enterprise reality.

• Work with cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP to support reliable, scalable AI workflows.

• Partner across product, engineering, business, and customer-facing teams to translate ambiguous business challenges into concrete AI solutions from concept through deployment and continuous improvement.

• Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI solutions using real-world enterprise datasets.

Required Qualifications

• 5+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science in industry, research labs, or advanced academic environments.

• Master's or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field

• Hands-on experience designing enterprise ontologies and semantic models; proficiency in at least one graph query language (SPARQL, Cypher, or GQL); understanding of trade-offs between RDF triple stores and property graph databases.

• Hands-on experience with modern GenAI systems RAG, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding.

• Strong Python and SQL skills with production-grade development practices; experience with ML libraries such as PyTorch, TensorFlow, or scikit-learn.

• Proven track record deploying and operating AI/ML solutions in production including handoff, lifecycle support, and continuous improvement.

• Experience with big data infrastructure and cloud environments Databricks or equivalent, plus at least one major cloud (AWS, Azure, or GCP).

• Excellent communication and stakeholder management skills, with the ability to work cross-functionally in agile environments.

Preferred Qualifications

• Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end. (SAP knowledge is a strong accelerator)

• Hands-on experience with the SAP data and AI platform stack SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub.

• Deep expertise across the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) and/or property graph query languages (Cypher, GQL).

• Demonstrated experience with data, semantics, and business processes of one or more major supply-chain domains (e.g.: demand, supply and inventory planning; product and bill-of-materials data; manufacturing and capacity; logistics and fulfillment; or asset and service operations) and connect entities, events, KPIs, constraints, and decisions across data domains.

• Deep expertise in one or more data science fields, including time-series analysis and forecasting, anomaly detection, causal inference, operations research, mathematical optimization, probabilistic modeling, or simulation and scenario search. Track record of productionizing models and measuring calibration, decision quality, and business outcomes.

• Deep expertise in machine learning and deep learning, with experience developing, evaluating, and improving models on real-world datasets.

• Experience with agentic AI, reasoning frameworks, planning, orchestration, tool use, or multi-agent architectures.

• Experience contributing to reusable AI platforms, foundation model initiatives, or shared AI services adopted across multiple product areas.

• Ability to design upper-level and mid-level ontologies aligned with industry standards and apply semantic interoperability frameworks across complex application landscapes.

Where you belong

Data Labs is building the data, semantic, and decision-intelligence foundation for SAP’s next generation of autonomous supply-chain capabilities. You'll work with large-scale planning and execution data and transform it into governed decision context, enabling AI agents to understand disruptions, trace their impact across applications, evaluate feasible responses, and act within enterprise guardrails. Products span supply-chain ontology packs, reusable semantic data products, forecasting and optimization models, typed agent interfaces, and rigorous evaluation suites. SAP Data Scientists work alongside domain experts, data engineers, ML engineers, and application teams across planning, manufacturing, logistics, product design, and asset operations. This is an opportunity to shape foundational technology used across SAP IBP, Joule, and Autonomous SCM agents — and to see that work translate directly into faster, higher-quality supply-chain decisions for customers.


AI skills used in this role: Agentic AI Day-to-Day Practice, AI Adoption Capability, AI Output Quality Assurance, Context Engineering, AI-Assisted Automation and Prototyping, Learning Agility, Creative Thinking, Complex Problem Solving, Effective Communication, Collaboration, Agentic Orchestration, Data Engineering, Deep Learning, Model Training, Semantic Retrieval
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