Data Science Expert- Finance & Spend, Data Labs

SAP SE; Bengaluru; On-site; INR 2,500,000 - 4,200,000; Full time

Job summary

SAP SE seeks a Data Science Expert for Finance & Spend in the Data Labs to design and deploy AI-driven solutions across core finance processes. You will ground AI in enterprise data, knowledge graphs, and structured data, enabling scalable, trusted AI across SAP’s finance landscape.

You will collaborate with product, engineering, and customer teams to turn ambiguous business challenges into concrete AI applications, delivering end-to-end solutions from concept through deployment.

Qualifications

• 8+ years in knowledge engineering, semantic data systems, applied AI or data science.
• Master's or PhD in CS, applied mathematics, statistics, engineering, or related field.
• Design enterprise ontologies and semantic models; graph query languages knowledge.
• Experience with GenAI, RAG, embeddings, vector databases and enterprise grounding.
• Strong Python and SQL with ML libraries (PyTorch, TensorFlow, scikit-learn).
• Experience deploying AI/ML in production with lifecycle support.

Responsibilities

• Build semantic foundations and ontologies for SAP AI agents.
• Integrate data from SAP, Salesforce, Workday and others into unified layers.
• Develop AI capabilities including RAG pipelines and embeddings.
• Collaborate with product, engineering, business to deploy AI solutions.
• Apply ML and statistical modeling to enterprise datasets.

Skills

Knowledge engineering

Semantic data systems

Applied AI

Python

SQL

ML libraries

Communication

Education

Master's or PhD in CS/Math/Stats/Engineering

Tools

SPARQL

Cypher

GQL

Databricks

SAP Datasphere

SAP HANA Cloud

TensorFlow

PyTorch

Job description

Data Science Expert- Finance & Spend, Data Labs

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.

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.

Whatyou'llbuild

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.

Whatyou'llbring

• 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.

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).
• Experience on Financial (example - accounting, close, reporting) and Spend (procurement, s2p, contracts) domain knowledge.
• 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

You'll join the Data Labs unit, a tight-knit team turning AI from a promise into something Finance and Spend teams rely on every day, at 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. The problems are real – money, risk, trust, and so is ownership. You'll stretch into new domains, see your models run, and help set the direction for SAP's AI in Finance and Spend. You will learn fast have an excellent opportunity to own things end to end and build foundations others will stand on.

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.

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