Principal Data and Applied Scientist, SCM Autonomous Suite, Bellevue

SAP; Bellevue (WA); On-site; USD 140,000 - 210,000; Full time

Job summary

SAP is seeking an experienced Data and Applied Scientist to build the context engine that grounds enterprise data for AI agents. You will design semantic models, integrate data across ERP landscapes, and deploy ML solutions in production.

The role emphasizes collaborating with product, engineering, and business teams, applying machine learning and statistical modeling to real datasets, and ensuring scalable AI workflows on cloud platforms.

Qualifications

• 8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science.
• Master's or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field.
• Hands-on designing enterprise ontologies and semantic models; proficiency in graph query languages such as SPARQL, Cypher, or GQL; understanding RDF vs property graphs.

Responsibilities

• Design and maintain enterprise ontologies and semantic models with grounded understanding of SAP and connected business landscapes.
• Build AI capabilities including RAG pipelines, embeddings, vector databases, and knowledge grounding for production reliability.

Skills

Knowledge engineering

Semantic data

Applied AI

Data science

Python

SQL

ML libraries

Databricks

Cloud platforms

Communication

Education

Master's degree in CS

PhD in related field

Tools

SPARQL

Cypher

GQL

PyTorch

TensorFlow

scikit-learn

Job description

We help the world run better

At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.

We help the world run better

At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.

The context engine that makes AI enterprise ready.

Anyone can build an AI agent. What makes SAP's agents different is accuracy grounded in the richest enterprise data and process context in the world. As a Data and Applied Scientist at SAP, you'll build the context engine grounded in SAP’s Business ontology: the semantic infrastructure that transforms raw business data into the knowledge layer powering SAP's AI agents and assistants.

What You'll Build

The semantic and contextual foundation of SAP's 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. You'll build and scale the layer that makes that possible.

• 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.

What You'll Bring

Required 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.

Preferred Qualifications

• Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end. (SAP knowledge is a strong accelerator)
• 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.
• Deep expertise across the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) and/or property graph query languages (Cypher, GQL).
• Demonstrated experience with data, semantics, and business processes of one or more major supply-chain domains (e.g.: demand, supply and inventory planning; product and bill-of-materials data; manufacturing and capacity; logistics and fulfillment; or asset and service operations) and connect entities, events, KPIs, constraints, and decisions across data domains.
• Deep expertise in one or more data science fields, including time-series analysis and forecasting, anomaly detection, causal inference, operations research, mathematical optimization, probabilistic modeling, or simulation and scenario search. Track record of productionizing models