Principal Data & Applied Scientist - Ontologies & Semantics

SAP•Palo Alto, CA

5d•$198,200 - $420,000•Hybrid

About The Position

As a Principal Data & Applied Scientist on SAP’s Application AI team, you will build and scale the semantic and contextual foundation that helps SAP’s AI agents accurately understand enterprise data and processes. The role focuses on SAP’s Business Ontology and on creating unified semantic layers from SAP data and external sources, including providers such as Salesforce, Workday, and ServiceNow, as well as MES/IoT systems. You will develop enterprise ontologies and semantic models, generative AI and LLM-based solutions, retrieval-augmented generation (RAG) pipelines, embeddings, vector databases, and knowledge-grounding capabilities. You will use SAP data models, metadata, and business-process semantics across areas such as Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce. The role also uses cloud and data platforms to support scalable AI workflows and applies machine learning, deep learning, and statistical modeling to real-world enterprise data. Working across product, engineering, business, and customer-facing teams, you will turn ambiguous challenges into AI solutions from concept through deployment and ongoing improvement. The team works at the foundation layer of SAP’s global platforms and values ownership, architectural leadership, trust, autonomy, and outcome-driven work. The position is listed as Regular Full Time. The targeted annual combined compensation range, inclusive of base salary and variable incentive target, is $198,200–$420,000 USD. Expected travel is 0–10%. Additional location information is marked #LI-Hybrid.

Requirements

At least 8 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, including RAG, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding.
Strong Python and SQL skills and 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, such as Databricks or equivalent, plus at least one major cloud provider: AWS, Azure, or GCP.
Excellent communication and stakeholder-management skills, with the ability to work cross-functionally in agile environments.

Nice To Haves

Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end.
Hands-on experience with SAP data and AI platform technologies: 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, including OWL, RDF/RDFS, SKOS, and SHACL, and/or property graph query languages such as Cypher and GQL.
Deep expertise in machine learning and deep learning, including 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.

Responsibilities

Design and maintain enterprise ontologies and semantic models that enable accurate, grounded understanding of SAP and connected business landscapes.
Harmonize data from SAP, external providers such as Salesforce, Workday, and ServiceNow, and MES/IoT systems into unified semantic layers.
Build AI capabilities including RAG pipelines, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding.
Develop generative AI and LLM-based solutions using enterprise business data, knowledge graphs, business process intelligence, and structured and unstructured data.
Use 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.
Work with cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP to support reliable, scalable AI workflows.
Partner with product, engineering, business, and customer-facing teams to translate business challenges into 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.
Deploy and operate AI/ML solutions in production, including handoff, lifecycle support, and continuous improvement.
Help define architecture and drive outcomes within the Application AI team’s enterprise-scale LLM systems and intelligent infrastructure.