Senior Data Scientist- Finance & Spend, Data Labs

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.

Whatyoullbuild

The semantic and contextual foundation of SAPs AI. While generic AI agentsoperateon surface-level patterns SAP agents areaccuratebecause they understand the real semantics of enterprise business master data process flows and domain and scale the layer that makes that possible.

• Design andmaintainenterprise ontologies and semantic modelsthat give AI agentsaccurate 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 capabilitiesincluding RAG pipelines embeddings vector databases and enterprise knowledgegrounding thatmake SAPs agentsaccurateand reliable in production.

• Develop AI capabilitiesincluding 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 processcontextincludingSAP 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 platformsincluding Databricks SAP Datasphere SAP HANA Cloud AWS Azure and GCP to support reliable scalable AI workflows.

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

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

Whatyoullbring

Required Qualifications

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

• Masters or PhDin Computer Science Applied Mathematics Statistics Engineering or a related quantitative field

• Hands-on experience designing enterprise ontologies and semantic models;proficiencyin 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 Python and SQL skillswith production-grade development practices; experience with ML libraries such asPyTorch TensorFlow or scikit-learn.

• Proventrack recorddeploying and operating AI/ML solutionsin production including handoff lifecycle support and continuous improvement.

• Experience with big data infrastructure and cloud environmentsDatabricks 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.

• 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.

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

• Experience on Financial (example - accounting closereporting) andSpend (procurement s2p contracts) domain knowledge

• Deepexpertisein 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.

Whereyoubelong

Youlljoin the Data Labs unit a tight-knit team turning AI from a promise into something Finance and Spend teams rely on every dayatglobal alongside curious engineers thoughtful product minds and applied researchers all focused on building AI that customers can trust in the highest-stakes business processes. The problems are real - money risk trust and so is into new domains see your models run and help set the direction for SAPs AI in Finance and Spend. You will learnfasthave an excellent opportunity to own things end toend andbuild foundations others will stand on.

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