Principal Data and Applied Science, HCM Autonomous Suite, Palo Alto

SAP•Palo Alto, CA

9d•Hybrid

About The Position

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. Join SAP SuccessFactors at a pivotal point in the evolution of enterprise AI, where we are moving beyond traditional applications and analytics toward intelligent, autonomous agents that can understand HCM context, reason over trusted business data, and help customers make better workforce decisions. As part of the SuccessFactors Line-of-Business, you will work at the intersection of HCM domain expertise, enterprise data, applied AI, and agentic technologies—helping shape how employees, managers, HR leaders, and businesses interact with SAP through intelligent experiences. You will collaborate with product, engineering, data, and research teams to ground autonomous agents in SuccessFactors business processes, semantics, policies, and data, while building solutions that are accurate, responsible, secure, and scalable. This is an opportunity to work on high-impact problems across the employee lifecycle, from recruiting and talent to workforce planning, analytics, and people intelligence, and to play a foundational role in defining the next generation of AI-powered HCM at SAP.

Requirements

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.

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).
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.
Understanding of HR/HCM domain semantics and the ability to translate SuccessFactors business concepts, processes, and terminology into semantic models, knowledge representations, AI grounding, and agentic experiences.
Experience working with SAP SuccessFactors extensibility and integration technologies, including OData APIs, Integration Center, SAP BTP, and/or other SAP integration patterns.
Experience applying analytics, machine learning, or statistical methods to workforce use cases such as attrition, headcount, workforce planning, skills, compensation, recruiting, performance, engagement, or talent intelligence.

Responsibilities

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.