Data Science Leader, CX

SAP•Bellevue, WA

13d•$282,500 - $609,200•Hybrid

About The Position

The Data and Applied Science team will 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. Your team will build and scale the layer that makes that possible. 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.

Requirements

10+ years proven experience leading, mentoring, and growing high-performing teams of data scientists, machine learning engineers, and AI practitioners, with a strong track record of driving complex AI initiatives from concept to production across multiple teams and stakeholders.
5+ years of people management experience leading data science and AI teams, with demonstrated success hiring, coaching, and retaining top AI talent.
Proven experience delivering large-scale AI programs across multiple teams and business units and partnering with senior executives to define AI strategy and investment priorities.
Excellent stakeholder management and executive communication skills, with the ability to influence senior leadership and translate technical concepts into business value.
Experience building a culture of technical excellence, operational rigor, and continuous learning.
Experience defining AI/ML architecture, platform strategy, model governance, responsible AI practices, and operational frameworks for enterprise-scale deployments.
Strong domain expertise in ontology engineering, semantic technologies, metadata management, entity resolution, taxonomies, and knowledge representation, with demonstrated experience designing, building, and scaling ontology-driven enterprise intelligence solutions, including knowledge graphs, semantic layers, and business knowledge models.
Deep knowledge of SAP application data models, domain processes, and enterprise data architecture, with the ability to apply this understanding to design ontologies, semantic layers, and knowledge graph solutions that accurately reflect SAP's business and application context.
Expertise in statistics, computer science, and mathematics, as well as proficiency in software tools and programming languages for Machine Learning Model Training.
Specialized abilities in mathematics, programming, and data analysis for Deep Learning.
High level of technical expertise in programming, data modeling, and data visualization for Data Engineering.
Skills in managing task decomposition, agent communication, error handling, quality human validation, and designing agent harnesses for long-running tasks, context durability, tool lifecycle, and sub-agent coordination for Agentic Orchestration.
Deep expertise in embedding models, retrieval optimization techniques, and modern data architecture patterns for Semantic Retrieval.

Nice To Haves

Experience leading globally distributed teams and cross-organizational AI initiatives, including managing budgets, hiring plans, vendor relationships, and strategic partnerships.
Thought leadership demonstrated through patents, publications, conference presentations, open-source contributions, or industry recognition in AI, knowledge graphs, semantic technologies, or enterprise intelligence.
Knowledge of SAP’s domains, data models, metadata structures and core business processes end-to-end.
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.
Ability to evaluate emerging AI technologies and establish best practices for ML Ops, LLM Ops, and model lifecycle management - making pragmatic build-versus-buy decisions across platform investments and technology roadmaps.
Experience integrating knowledge graphs and ontology layers with machine learning, generative AI, agentic AI, RAG architectures, and enterprise data platforms to improve reasoning, explainability, grounding, and business context.
Ability to collaborate with domain experts and business stakeholders to translate complex business processes, data assets, and enterprise knowledge into reusable semantic and knowledge graph frameworks.

Responsibilities

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.
Build and scale the layer that makes SAP's AI agents accurate by understanding the real semantics of enterprise business master data, process flows, and domain relationships.
Define technical strategy, establish team priorities, and align AI investments with business objectives, product roadmaps, and customer outcomes.
Build a culture of technical excellence, operational rigor, and continuous learning.
Manage budgets, hiring plans, vendor relationships, and strategic partnerships.
Define AI adoption strategies, measure business impact through KPIs, and establish reusable enterprise AI platforms, governance frameworks, and shared services across multiple product areas.
Define AI/ML architecture, platform strategy, model governance, responsible AI practices, and operational frameworks for enterprise-scale deployments.
Evaluate emerging AI technologies and establish best practices for ML Ops, LLM Ops, and model lifecycle management - making pragmatic build-versus-buy decisions across platform investments and technology roadmaps.
Design, build, and scale ontology-driven enterprise intelligence solutions, including knowledge graphs, semantic layers, and business knowledge models.
Integrate knowledge graphs and ontology layers with machine learning, generative AI, agentic AI, RAG architectures, and enterprise data platforms to improve reasoning, explainability, grounding, and business context.
Collaborate with domain experts and business stakeholders to translate complex business processes, data assets, and enterprise knowledge into reusable semantic and knowledge graph frameworks.
Design ontologies, semantic layers, and knowledge graph solutions that accurately reflect SAP's business and application context.
Conduct Machine Learning Model Training, including selecting the right algorithm, preparing data sets, and testing model accuracy.
Utilize deep learning and neural networks to enable machines to learn from data and make decisions.
Design, build, and maintain infrastructure for data collection, transformation, storage, and analysis.
Design, delegate, and supervise multi-agent workflows involving autonomous AI agents with multi-step reasoning and coordination, including task decomposition, agent communication, error handling, quality human validation, and designing agent harnesses for long-running tasks, context durability, tool lifecycle, and sub-agent coordination.
Design, implement, and optimize semantic search and retrieval architectures including RAG pipelines, knowledge graphs, and hybrid vector/keyword search systems and Lakehouse architectures.