Data Scientist

Data and Applied Science

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 help 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.

This is an early-career role for engineers and scientists who are sharp curious and ready to do real work on hard problems from day one.

What youll build

Youll contribute to 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 work alongside senior scientists and engineers to build and scale the layer that makes that possible.

• Support the design and maintenance of enterprise ontologies and semantic models that give AI agents accurate grounded understanding of SAP and connected business landscapes learning how data from SAP Salesforce Workday ServiceNow MES/IoT systems and external providers gets harmonized into unified semantic layers.

• Contribute to AI capabilities including RAG pipelines embeddings vector databases and enterprise knowledge grounding that make SAPs agents accurate and reliable in production.

• Develop and iterate on AI solutions including generative AI and LLM-based approaches using enterprise business data knowledge graphs business process intelligence and structured and unstructured data assets.

• Learn SAPs deep data and process context data models metadata structures and business process semantics across Order-to-Cash Procure-to-Pay Record-to-Report and Plan-to-Produce and apply that context to ground AI solutions in real enterprise reality.

• Work with modern cloud and data platforms including Databricks SAP Datasphere SAP HANA Cloud AWS Azure and GCP gaining hands-on experience with scalable AI workflows.

• Collaborate across product engineering and business teams to understand how ambiguous business challenges get translated into concrete AI solutions and contribute meaningfully to that process from early stages through deployment.

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

What youll bring

Required Qualifications

• Bachelors or Masters in Computer Science Applied Mathematics Statistics Engineering or a related quantitative field recent graduates welcome.

• 3 years of CS CE ML or related field experience work

• Foundational understanding of knowledge representation semantic data systems or graph databases (through coursework research or personal projects).

• Familiarity with at least one graph query language (SPARQL Cypher or GQL) or a willingness to learn quickly; some exposure to the trade-offs between RDF triple stores and property graph databases is a plus.

• Exposure to modern GenAI concepts RAG embeddings vector databases semantic retrieval through coursework research or hands-on experimentation.

• Solid Python and SQL skills; some experience with ML libraries such as PyTorch TensorFlow or scikit-learn (academic projects research work and personal projects all count).

• Eagerness to learn production-grade development practices and grow into operating AI/ML solutions end-to-end.

• Clear collaborative communication style you ask good questions explain your thinking and work well with others.

Preferred Qualifications

• Hands-on experience through internships research or projects with ontology design semantic modeling or knowledge graphs.

• Any exposure to enterprise software ecosystems (SAP Salesforce Workday ServiceNow or similar) is a real accelerator here.

• Familiarity with the W3C stack (OWL RDF/RDFS SKOS SHACL) or property graph query languages (Cypher GQL).

• Academic or project experience in machine learning and deep learning including training evaluating and improving models on real datasets.

• Curiosity about agentic AI reasoning frameworks multi-agent architectures or planning and orchestration.

• Experience contributing to shared or reusable codebases open-source projects research codebases or team projects.

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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