Data Science Chief Expert, Domain Models

SAP•Stanford, CA

47d•$274,300 - $609,200•Hybrid

About The Position

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 part of our Data and Applied Science team, 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 Data and Applied Science team will 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 will help build and scale the layer that makes that possible. This may include the following: Leverage deep SAP data and process understanding — including SAP data models, metadata structures, and end-to-end business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce — to build AI and semantic data solutions using SAP master data domains, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub assets. Design and maintain enterprise ontologies and semantic models to improve interoperability, entity consistency, and business context across SAP and non-SAP data landscapes, harmonizing sources such as Salesforce, Workday, ServiceNow, MES/IoT systems, and external data providers into unified semantic or analytical layers. Work with cloud and data platforms such as Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, or Google Cloud Platform to support reliable AI workflows. Translate ambiguous business challenges into concrete AI use cases, technical designs, and measurable business outcomes. Design, develop, evaluate, and operationalize end-to-end machine learning and AI solutions — from data preprocessing, feature engineering, experimentation, and validation through to deployment, production handoff, lifecycle support, and continuous improvement. Apply advanced methods across machine learning, deep learning, statistical modeling, data mining, optimization, and applied AI to solve enterprise-scale problems. 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. Partner closely with product, engineering, business, and customer-facing teams to ensure solutions are scalable, practical, and production-ready.

Requirements

Master's degree or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative fields.
10+ years of experience to include deep expertise in machine learning, deep learning, statistical modeling, generative AI, and LLMs, with hands-on experience developing, evaluating, and improving models using real-world datasets — including data preprocessing, feature engineering, and experimentation — and strong analytical and mathematical modeling skills.
10+ years of experience in machine learning, data science, applied AI, AI research, knowledge engineering, or semantic data systems in industry, research labs, or advanced academic environments.
Strong Python and SQL skills, including production-grade Python development and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn.
Demonstrated experience of deploying, shipping, and operating AI or machine learning solutions in production environments, including production handoff and lifecycle support.
Experience with big data infrastructure, data processing and transformation tools such as Databricks, and cloud environments such as AWS, Azure, or Google Cloud Platform.
Excellent communication, collaboration, and customer-facing skills, with significant experience in agile development environments and a strong curiosity for exploring new AI techniques and their practical applications for SAP customers and products.
Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end, including how process semantics map to underlying business objects and datasets.
Hands-on experience with the SAP data and AI platform stack — including SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub — with working knowledge of SAP master data domains and Master Data Governance constructs.
Hands-on experience designing and maintaining enterprise ontologies using OWL, RDF/RDFS, SKOS, and SHACL, with proficiency in SPARQL, Cypher, and GQL, and experience evaluating trade-offs between RDF triple stores and labeled property graph databases.
Experience building entity resolution, deduplication, and identity stitching pipelines across SAP and non-SAP systems, with proven ability to harmonize data into a unified semantic layer using federation, virtualization, replication, and shared ontology mapping approaches.
Understanding data product and data mesh principles, including semantic contracts and governed self-service consumption.
Proven experience translating abstract business challenges into concrete AI solutions, delivering from concept through production deployment, production handoff, and business adoption.
Experience working with cross-functional stakeholders — including product, engineering, business, and customer-facing teams — in agile software development environments and enterprise product organizations.
Experience building AI capabilities using enterprise business data, knowledge graphs, or business process intelligence.

Nice To Haves

Experience with Retrieval-Augmented Generation, vector databases, embeddings, semantic retrieval, and enterprise knowledge grounding.
Experience contributing to reusable AI platforms, foundation model initiatives, shared AI services, or AI capabilities adopted across multiple product areas.
Experience with agentic AI, reasoning frameworks, planning, orchestration, tool use, or multi-agent architectures.
Ability to design upper-level and mid-level ontologies aligned with industry standards, map application-specific schemas to shared ontologies using declarative mapping standards and apply semantic interoperability frameworks and canonical business entity models across complex application landscapes.

Responsibilities

Leverage deep SAP data and process understanding — including SAP data models, metadata structures, and end-to-end business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce — to build AI and semantic data solutions using SAP master data domains, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub assets.
Design and maintain enterprise ontologies and semantic models to improve interoperability, entity consistency, and business context across SAP and non-SAP data landscapes, harmonizing sources such as Salesforce, Workday, ServiceNow, MES/IoT systems, and external data providers into unified semantic or analytical layers.
Work with cloud and data platforms such as Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, or Google Cloud Platform to support reliable AI workflows.
Translate ambiguous business challenges into concrete AI use cases, technical designs, and measurable business outcomes.
Design, develop, evaluate, and operationalize end-to-end machine learning and AI solutions — from data preprocessing, feature engineering, experimentation, and validation through to deployment, production handoff, lifecycle support, and continuous improvement.
Apply advanced methods across machine learning, deep learning, statistical modeling, data mining, optimization, and applied AI to solve enterprise-scale problems.
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.
Partner closely with product, engineering, business, and customer-facing teams to ensure solutions are scalable, practical, and production-ready.