Data Science Chief Expert, Spend

Quick Facts

• Role: Data Science Chief Expert (Spend)

Description

Lead the development of enterprise-ready AI context and semantic capabilities for SAP’s agents, grounded in business ontologies and process semantics. Translate business challenges into AI use cases, design and operationalize end-to-end ML/AI solutions, and deploy them into production with lifecycle support and continuous improvement. Build and scale ontology and semantic data layers across SAP and non-SAP data landscapes, partnering closely with product, engineering, and business stakeholders.

Responsibilities

• Leverage deep SAP data and process understanding (data models, metadata structures, and end-to-end business process semantics) to build AI and semantic data solutions.

• Design and maintain enterprise ontologies and semantic models to improve interoperability, entity consistency, and business context across SAP and non-SAP data sources.

• Harmonize sources such as Salesforce, Workday, ServiceNow, and MES/IoT into unified semantic or analytical layers.

• Build AI workflows using cloud/data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, or Google Cloud Platform.

• Convert ambiguous business needs into concrete AI use cases, technical designs, and measurable outcomes.

• Design, develop, evaluate, and operationalize end-to-end machine learning and AI solutions from preprocessing and feature engineering through deployment and lifecycle support.

• Apply advanced ML, deep learning, statistical modeling, data mining, optimization, and applied AI methods to solve enterprise-scale problems.

• Develop AI capabilities including generative AI and LLM-based solutions using enterprise business data, knowledge graphs, and business process intelligence.

• Partner 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 experience with ML, deep learning, statistical modeling, generative AI, and LLMs; hands-on model development, evaluation, and improvement with real-world datasets.

• 10+ years experience in ML, data science, applied AI, AI research, knowledge engineering, or semantic data systems.

• Strong Python and SQL skills, including production-grade Python and ML libraries (PyTorch, TensorFlow, scikit-learn).

• Experience deploying and operating ML/AI solutions in production, including production handoff and lifecycle support.

• Big data infrastructure and cloud/data platform experience (Databricks; AWS/Azure/GCP).

• Deep knowledge of SAP data models, metadata structures, and end-to-end business processes and how process semantics map to business objects.

• Hands-on SAP data/AI platform stack experience (SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, SAP Business Accelerator Hub).

• Hands-on ontology/semantic modeling using OWL, RDF/RDFS, SKOS, SHACL; SPARQL plus Cypher/GQL; experience comparing RDF triple stores vs labeled property graph databases.

• Experience building entity resolution, deduplication, and identity stitching pipelines across SAP and non-SAP systems and harmonizing data into a unified semantic layer.

• Understanding of data product/data mesh principles, including semantic contracts and governed self-service consumption.

• Proven ability to translate abstract business challenges into AI solutions through deployment and adoption.

• Experience collaborating with cross-functional stakeholders in agile software development.

• Experience building AI capabilities using enterprise business data, knowledge graphs, or business process intelligence.

Benefits

• Continuous learning and skill growth

• Great benefits and wellbeing support

• Collaborative team environment with opportunities for technical growth