Data Science Leader - Finance

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.

What you'll build:

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.

What you'll bring:

Requirements for this position include the following:

Leadership & Strategic Skills

• 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.

• Ability to define technical strategy, establish team priorities, and align AI investments with business objectives, product roadmaps, and customer outcomes.

• Experience building a culture of technical excellence, operational rigor, and continuous learning.

• Excellent stakeholder management and executive communication skills, with the ability to influence senior leadership and translate technical concepts into business value.

• 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.

Desirable Skills

• 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.

Key Knowledge and Abilities:

AI Platform, Architecture & Governance

• Experience defining AI/ML architecture, platform strategy, model governance, responsible AI practices, and operational frameworks for enterprise-scale deployments.

• 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.

Knowledge Graphs, Ontologies & Enterprise Intelligence

• 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.

• 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.

• 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.

Data and Applied Science – General Job Profile Skills

Model Training

• 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.

Deep Learning

• 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.

Data Engineering

• 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.

Agentic Orchestration

• 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.

Semantic Retrieval

• 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.

Where you belong:

Join a collaborative, forward-thinking team defining how enterprise AI actually works on a 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. There's room to grow into new technical spaces, ship real impact, and shape SAP's AI future. If you value learning, real ownership, and building foundational infrastructure that matters, you'll feel at home here.

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