Data Science Chief Expert, Spend

About the role

The Data and Applied Science team at SAP is seeking a skilled individual to build the semantic and contextual foundation of SAP's AI. This role involves leveraging deep SAP data and process understanding to build AI and semantic data solutions, working with cloud and data platforms, and translating business challenges into concrete AI use cases.

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.
• 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

Required qualifications include a Master's degree or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative fields, with 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, are also required. Experience with big data infrastructure, data processing and transformation tools such as Databricks, and cloud environments such as AWS, Azure, or Google Cloud Platform is preferred. 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, are essential.

Qualifications

• 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.
• Experience with big data infrastructure, data processing and transformation tools such as Databricks, and cloud environments such as AWS, Azure, or Google Cloud Platform.
• Strong Python and SQL skills, including production-grade Python development and experience with ML libraries such as PyTorch, TensorFlow, and scikit-learn.
• 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.

Preferred Qualification

• 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.

Benefits

At SAP, you can expect a competitive compensation range, comprehensive benefits package, and a supportive culture that fosters personal and professional growth. SAP is committed to equal employment opportunity and providing accessible accommodations to applicants with physical and/or mental disabilities. For more information on SAP's commitment to diversity, inclusion, and ethical practices, visit our website.

Pay

The targeted annual combined range for this position is 274,300-609,200 USD.

Schedule

Expected travel: 0 - 20%