Job description

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 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 a Data and Applied Scientist at SAP, 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 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'll build and scale the layer that makes that possible. • Design and maintain enterprise ontologies and semantic models that give AI agents accurate, grounded understanding of SAP and connected business landscapes harmonizing data from SAP, Salesforce, Workday, ServiceNow, MES/IoT systems, and external providers into unified semantic layers. • Build AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding that make SAP's agents accurate and reliable in production. • 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. • Leverage SAP's deep data and process context including SAP data models, metadata structures, and business process semantics across Order-to-Cash, Procure-to-Pay, Record-to-Report, and Plan-to-Produce to ground AI solutions in real enterprise reality. • Work with cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP to support reliable, scalable AI workflows. • Partner across product, engineering, business, and customer-facing teams to translate ambiguous business challenges into concrete AI solutions from concept through deployment and continuous improvement. • Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI solutions using real-world enterprise datasets. What you'll bring Required Qualifications • 8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science in industry, research labs, or advanced academic environments. • 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 including handoff, lifecycle support, and continuous improvement. • Experience with big data infrastructure and cloud environments Databricks or equivalent, plus at least one major cloud (AWS, Azure, or GCP). • Excellent communication and stakeholder management skills, with the ability to work cross-functionally in agile environments. Preferred Qualifications • Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end. (SAP knowledge is a strong accelerator) • Hands-on experience with the SAP data and AI platform stack SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub. • Deep expertise across the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) and/or property graph query languages (Cypher, GQL). • Experience on Financial (example - accounting, close, reporting) and Spend (procurement, s2p, contracts) domain knowledge • Deep expertise in machine learning and deep learning, with experience developing, evaluating, and improving models on real-world datasets. • Experience with agentic AI, reasoning frameworks, planning, orchestration, tool use, or multi-agent architectures. • Experience contributing to reusable AI platforms, foundation model initiatives, or shared AI services adopted across multiple product areas. • Ability to design upper-level and mid-level ontologies aligned with industry standards and apply semantic interoperability frameworks across complex application landscapes. #dlhiring #LI-MM10 Where you belong You'll join the Data Labs unit, a tight-knit team turning AI from a promise into something Finance and Spend teams rely on every day, at global scale. You'll work alongside curious engineers, thoughtful product minds, and applied researchers all focused on building AI that customers can trust in the highest-stakes business processes. The problems are real - money, risk, trust, and so is ownership. You'll stretch into new domains, see your models run, and help set the direction for SAP's AI in Finance and Spend. You will learn fast have an excellent opportunity to own things end to end and build foundations others will stand on. Bring out your best SAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a market leader in end-to-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose-driven and future-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best. We win with inclusion SAP’s culture of inclusion, focus on health and well-being, and flexible working models help ensure that everyone – regardless of background – feels included and can run at their best. At SAP, we believe we are made stronger by the unique capabilities and qualities that each person brings to our company, and we invest in our employees to inspire confidence and help everyone realize their full potential. We ultimately believe in unleashing all talent and creating a better world. SAP is committed to the values of Equal Employment Opportunity and provides accessibility accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please send an e-mail with your request to Recruiting Operations Team: Careers@sap.com. For SAP employees: Only permanent roles are eligible for the SAP Employee Referral Program , according to the eligibility rules set in the SAP Referral Policy. Specific conditions may apply for roles in Vocational Training. Qualified applicants will receive consideration for employment without regard to their age, race, religion, national origin, ethnicity, age, gender (including pregnancy, childbirth, et al), sexual orientation, gender identity or expression, protected veteran status, or disability. Compensation Range Transparency : SAP believes the value of pay transparency contributes towards an honest and supportive culture and is a significant step toward demonstrating SAP’s commitment to pay equity. SAP provides the annualized compensation range inclusive of base salary and variable incentive target for the career level applicable to the posted role. The targeted annual combined range for this position is $198,200 - $420,000 (USD) . The actual amount to be offered to the successful candidate will be within that range, dependent upon the key aspects of each case which may include education, skills, experience, scope of the role, location, etc. as determined through the selection process. Any SAP variable incentive includes a targeted dollar amount and any actual payout amount is dependent on company and personal performance. Please reference this link for a summary of SAP benefits and eligibility requirements: SAP North America Benefits . AI Usage in the Recruitment Process For information on the responsible use of AI in our recruitment process, please refer to our Guidelines for Ethical Usage of AI in the Recruiting Process . Please note that any violation of these guidelines may result in disqualification from the hiring process. Requisition ID: 459701 | Work Area: Software-Design and Development | Expected Travel: 0 - 10% | Career Status: Professional | Employment Type: Regular Full Time | Additional Locations: #LI-Hybrid

Requirements

Must-haves

We help the world run better
The context engine that makes AI enterprise ready.
What you'll build
Build AI capabilities including RAG pipelines, embeddings, vector databases, and enterprise knowledge grounding that make SAP's agents accurate and reliable in production.
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.
Work with cloud and data platforms including Databricks, SAP Datasphere, SAP HANA Cloud, AWS, Azure, and GCP to support reliable, scalable AI workflows.
Partner across product, engineering, business, and customer-facing teams to translate ambiguous business challenges into concrete AI solutions from concept through deployment and continuous improvement.
Apply machine learning, deep learning, and statistical modeling to develop and evaluate AI solutions using real-world enterprise datasets.
8+ years of experience in knowledge engineering, semantic data systems, applied AI, or data science in industry, research labs, or advanced academic environments.
Master's or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or a related quantitative field
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 including handoff, lifecycle support, and continuous improvement.
Experience with big data infrastructure and cloud environments Databricks or equivalent, plus at least one major cloud (AWS, Azure, or GCP).
Excellent communication and stakeholder management skills, with the ability to work cross-functionally in agile environments.
Where you belong

Nice-to-haves

Deep working knowledge of SAP data models, metadata structures, and core business processes end-to-end. (SAP knowledge is a strong accelerator)
Hands-on experience with the SAP data and AI platform stack SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Business Data Cloud, SAP One Domain Model, SAP Graph API, and SAP Business Accelerator Hub.
Deep expertise across the W3C stack (OWL, RDF/RDFS, SKOS, SHACL) and/or property graph query languages (Cypher, GQL).
Experience on Financial (example - accounting, close, reporting) and Spend (procurement, s2p, contracts) domain knowledge
Deep expertise in machine learning and deep learning, with experience developing, evaluating, and improving models on real-world datasets.
Experience with agentic AI, reasoning frameworks, planning, orchestration, tool use, or multi-agent architectures.
Experience contributing to reusable AI platforms, foundation model initiatives, or shared AI services adopted across multiple product areas.
Ability to design upper-level and mid-level ontologies aligned with industry standards and apply semantic interoperability frameworks across complex application landscapes.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmawsazuregcpmachine learningpythonsqlmlagileapiaccessibility

Platform: successfactors
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