Data Science Chief Expert, Domain Models

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 AIenterprise 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 enginegrounded 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. What you'll bring: Required Qualifications 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 #J-18808-Ljbffr Socket