Senior Data and Applied Scientist, SCM Autonomous Suite, Bellevue

SAP; Bellevue (WA); On-site; USD 145,000 - 299,000; Full time

Job summary

SAP is seeking a Data and Applied Scientist to build the semantic foundation for enterprise AI agents. You will design ontologies, develop embeddings and RAG pipelines, and ground AI solutions in SAP's data landscape across SAP Datasphere, HANA Cloud, and external sources.

You will work with cloud platforms (AWS, Azure, GCP) and collaborate with product, engineering, and business teams to deploy AI solutions at scale, driving measurable business outcomes.

Qualifications

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

Responsibilities

• Build and scale the semantic and contextual foundation of SAP's AI agents.
• Design enterprise ontologies and semantic models that integrate data from SAP and external sources.
• Develop AI capabilities including generative AI and LLM-based solutions using enterprise data.
• Collaborate with product, engineering, business, and customer-facing teams from concept to deployment.
• Apply ML and deep learning to real-world enterprise datasets and evaluate outcomes.

Skills

Knowledge engineering

Semantic data systems

Applied AI

Data science

Python

SQL

ML libraries (PyTorch/TensorFlow/Sciki

Big data platforms

Cloud environments (Databricks, AWS/AZ

Communication skills

Education

Master's or PhD in Computer Science / Applied Mathematics / Statistics / Engineering

Tools

Databricks

SAP Datasphere

SAP HANA Cloud Knowledge Graph Engine

SAP Graph API

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

• 5+ 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).
• Demonstrated experience with data, semantics, and business processes of one or more major supply‑chain domains (e.g.: demand, supply and inventory planning; product and bill‑of‑materials data; manufacturing and capacity; logistics and fulfillment; or asset and service operations) and connect entities, events, KPIs, constraints, and decisions across data domains.
• Deep expertise in one or more data science fields, including time‑series analysis and forecasting, anomaly detection, causal inference, operations research, mathematical optimization, probabilistic modeling, or simulation and scenario search. Track record of productionizing models and measuring calibration, decision quality, and business outcomes.
• 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.

Where you belong

Data Labs is building the data, semantic, and decision‑intelligence foundation for SAP’s next generation of autonomous supply‑chain capabilities. You'll work with large‑scale planning and execution data and transform it into governed decision context, enabling AI agents to understand disruptions, trace their impact across applications, evaluate feasible responses, and act within enterprise guardrails. Products span supply‑chain ontology packs, reusable semantic data products, forecasting and optimization models, typed agent interfaces, and rigorous evaluation suites. SAP Data Scientists work alongside domain experts, data engineers, ML engineers, and application teams across planning, manufacturing, logistics, product design, and asset operations. This is an opportunity to shape foundational technology used across SAP IBP, Joule, and Autonomous SCM agents — and to see that work translate directly into faster, higher‑quality supply‑chain decisions for customers.

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: [e-mail].

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 towards 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 $145,000-$298,900. 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.