Data Science Leader, CX

SAP•Stanford, CA

47d•$244,700 - $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. We help the world run betterAt 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 part of our Data and Applied Science team, 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.

Requirements

10+ years proven experience leading, mentoring, and growing high-performing teams of data scientists, machine learning engineers, and AI practitioners.
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.
Demonstrated success hiring, coaching, and retaining top AI talent.
Proven experience delivering large-scale AI programs across multiple teams and business units.
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.
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.
Expertise in statistics, computer science, and mathematics.
Proficiency in software tools and programming languages for 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.
Expertise 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.
Experience defining AI adoption strategies, measuring business impact through KPIs, and establishing reusable enterprise AI platforms, governance frameworks, and shared services across multiple product areas.
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.
Ability to conduct Machine Learning Model Training. Employees who excel in this skill have expertise in statistics, computer science, and mathematics, as well as proficiency in software tools and programming languages. They are capable of selecting the right algorithm for a problem, preparing data sets, and testing model accuracy.
Ability to utilize deep learning and neural networks to enable machines to learn from data and make decisions. Employees who excel in this skill have specialized abilities in mathematics, programming, and data analysis, making them highly coveted in the tech industry.
Ability to design, build, and maintain infrastructure for data collection, transformation, storage, and analysis. Employees who excel in this skill have a high level of technical expertise in programming, data modeling, and data visualization.
Ability to design, delegate, and supervise multi-agent workflows involving autonomous AI agents with multi-step reasoning and coordination. Employees who excel in this skill manage task decomposition, agent communication, error handling, quality human validation, and design agent harnesses for long-running tasks, context durability, tool lifecycle, and sub-agent coordination.
Ability to design, implement, and optimize semantic search and retrieval architectures including RAG pipelines, knowledge graphs, and hybrid vector/keyword search systems and Lakehouse architectures. Employees who excel in this skill have deep expertise in embedding models, retrieval optimization techniques, and modern data architecture patterns.

Responsibilities

Define technical strategy, establish team priorities, and align AI investments with business objectives, product roadmaps, and customer outcomes.
Build and scale the layer that makes SAP's AI agents accurate by understanding enterprise business master data, process flows, and domain relationships.
Lead, mentor, and grow high-performing teams of data scientists, machine learning engineers, and AI practitioners.
Drive complex AI initiatives from concept to production across multiple teams and stakeholders.
Build a culture of technical excellence, operational rigor, and continuous learning.
Manage stakeholder relationships and communicate effectively with executives, translating technical concepts into business value.
Hire, coach, and retain top AI talent.
Deliver large-scale AI programs across multiple teams and business units.
Partner with senior executives to define AI strategy and investment priorities.
Lead globally distributed teams and cross-organizational AI initiatives, including managing 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.
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.
Collaborate with domain experts and business stakeholders to translate complex business processes, data assets, and enterprise knowledge into reusable semantic and knowledge graph frameworks.
Apply deep knowledge of SAP application data models, domain processes, and enterprise data architecture to design ontologies, semantic layers, and knowledge graph solutions.
Conduct Machine Learning Model Training, 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.
Design, implement, and optimize semantic search and retrieval architectures including RAG pipelines, knowledge graphs, and hybrid vector/keyword search systems and Lakehouse architectures.