Senior Data & Applied Scientist - Ontologies & Semantics

SAP•Palo Alto, CA

5d•$148,600 - $306,300•Hybrid

About The Position

As a Senior Data and Applied Scientist at SAP, you will build the semantic and contextual foundation that makes SAP’s AI agents and assistants accurate and enterprise-ready. You will create and scale the context engine grounded in SAP’s Business Ontology, transforming raw business data into knowledge that supports AI grounded in enterprise master data, business processes, and domain relationships. The role harmonizes data from SAP and external providers such as Salesforce, Workday, and ServiceNow, as well as MES and IoT systems, into unified semantic layers. You will develop enterprise ontologies and semantic models, generative AI and LLM-based solutions, RAG pipelines, embeddings, vector database capabilities, knowledge graphs, and enterprise knowledge grounding. You will use SAP data models, metadata structures, and process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce. The work spans cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP, and includes applying machine learning, deep learning, and statistical modeling to real-world enterprise datasets. You will collaborate with product, engineering, business, and customer-facing teams to turn ambiguous challenges into AI solutions from concept through deployment and continuous improvement. The Application AI team builds LLM systems and intelligent infrastructure used across SAP’s global platforms. The team values ownership, architectural thinking, trust, autonomy, and moving quickly to drive outcomes. This is a regular, full-time professional role. Expected travel is 0–10%. The targeted annual combined compensation range, inclusive of base salary and variable incentive target, is $148,600–$306,300 USD. The position is listed as #LI-Hybrid.

Requirements

At least 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, 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, including Databricks or equivalent and at least one major cloud provider (AWS, Azure, or GCP).
Excellent communication and stakeholder management skills, with ability to work cross-functionally in agile environments.

Nice To Haves

Deep working knowledge of SAP data models, metadata structures, and end-to-end core business processes.
Hands-on experience with 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.

Responsibilities

Design and maintain enterprise ontologies and semantic models that enable AI agents to understand 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 such as 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 assets.
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 scalable AI workflows.
Partner with product, engineering, business, and customer-facing teams to translate ambiguous 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.