Principal Data & Applied AI Scientist, Enterprise Context Engines

SAP SE

Palo Alto (CA)

On-site

USD 198,000 - 420,000

Full time

10 days ago

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Job summary

SAP SE is seeking a Principal Data and Applied Scientist for the SCM Autonomous Suite in Palo Alto. You will build the semantic foundation of SAP's AI agents, design enterprise ontologies, and scale RTV-based pipelines across SAP data landscapes.

You will work across cloud platforms (Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, GCP) and collaborate with product, engineering, and business teams to translate complex problems into practical AI solutions, evolving policies and governance

Qualifications

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 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.

Responsibilities

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 SAP's 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 SAP's 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.