Senior Data Scientist

The context engine that makes AI enterprise ready.

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

What youll build

The semantic and contextual foundation of SAPs 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. Youll 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 SAPs 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 SAPs 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.

What youll bring

Required Qualifications

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

• Masters 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).

• 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.

• Understanding of HR/HCM domain semantics and the ability to translate SuccessFactors business concepts processes and terminology into semantic models knowledge representations AI grounding and agentic experiences.

• Experience working with SAP SuccessFactors extensibility and integration technologies including OData APIs Integration Center SAP BTP and/or other SAP integration patterns.

• Experience applying analytics machine learning or statistical methods to workforce use cases such as attrition headcount workforce planning skills compensation recruiting performance engagement or talent intelligence.

Where you belong

Join SAP SuccessFactors at a pivotal point in the evolution of enterprise AI where we are moving beyond traditional applications and analytics toward intelligent autonomous agents that can understand HCM context reason over trusted business data and help customers make better workforce decisions. As part of the SuccessFactors Line-of-Business you will work at the intersection of HCM domain expertise enterprise data applied AI and agentic technologieshelping shape how employees managers HR leaders and businesses interact with SAP through intelligent experiences. You will collaborate with product engineering data and research teams to ground autonomous agents in SuccessFactors business processes semantics policies and data while building solutions that are accurate responsible secure and scalable. This is an opportunity to work on high-impact problems across the employee lifecycle from recruiting and talent to workforce planning analytics and people intelligence and to play a foundational role in defining the next generation of AI-powered HCM at SAP.

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