Senior Data & Applied Scientist - Ontologies & Semantics

Quick Facts

• Role: Senior Data and Applied Scientist

Description

The position focuses on building a semantic and contextual foundation that makes enterprise AI agents accurate and reliable by grounding them in rich enterprise data and process context. You will design and maintain enterprise ontologies and semantic models using SAP’s Business ontology, and develop AI capabilities such as RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding. You will also develop LLM-based solutions using enterprise business data, knowledge graphs, business process intelligence, and structured/unstructured data assets.

Responsibilities

• Design and maintain enterprise ontologies and semantic models for AI agents’ grounded understanding of SAP and connected business landscapes
• Harmonize data from SAP, external providers (e.g., Salesforce, Workday, ServiceNow), and MES/IoT systems into unified semantic layers
• Build AI capabilities: RAG pipelines, embeddings, vector databases, semantic retrieval, and enterprise knowledge grounding
• Develop generative AI and LLM-based solutions using enterprise business data, knowledge graphs, business process intelligence, and structured/unstructured assets
• Leverage SAP data models, metadata structures, and business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce
• Work with cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP to support scalable AI workflows
• Collaborate across product, engineering, business, and customer-facing teams to translate business challenges into deployed AI solutions with continuous improvement
• Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI solutions using real-world enterprise datasets

Requirements

• 5+ years 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 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 (handoff, lifecycle support, continuous improvement)
• Experience with big data infrastructure and cloud environments (Databricks or equivalent) plus at least one of AWS, Azure, or GCP
• Excellent communication and stakeholder management skills; ability to work cross-functionally in agile environments

Benefits

• Constant learning and skill growth
• Great benefits
• Team environment focused on growth and success
• Trust and autonomy; move fast and drive outcomes

Compensation Range Transparency

Targeted annual combined range: 148600–306300 USD (base salary plus targeted variable incentive), dependent on education, skills, experience, and scope of the role.

Other

• Expected travel: 0-10%