Senior Data Scientist - Autonomous Compliance Foundation

What you'll bring

You bring applied data science depth: a working command of applied statistics and machine learning, including deep learning, foundation models, and semantic retrieval. Feature engineering, model training and monitoring, and the ability to reason rigorously about data quality and model behavior are core to your toolkit. Embedding ethical AI, governance, data privacy, and PII handling into the systems you build is part of how you approach the work.

This is built on a solid engineering foundation. Hands-on software development experience (Python or a comparable language) and applicable experience with enterprise or large-scale production systems (such as ERP or SaaS platforms) are both required. You have a robust knowledge of distributed data systems and cloud-native data architectures, hands-on experience designing and operating complex data pipelines (batch and real-time), and robust system-design, performance-optimization, and problem-solving skills. Technical management and the ability to guide and mentor others round out what you bring.

Nice to have (not required): comprehension of e-invoicing or EDI document formats (XML, JSON, Peppol BIS) — or the ability to ramp up fast on domain-specific formats.

What you'll build

You'll join the Autonomous Compliance Foundation team. The main focus is autonomous compliance: turning new regulatory and legal requirements into working, compliant software — from legal text to running code — through an automated pipeline with human validation gates that reduces the manual effort of implementing legal changes. The pipeline also produces the downstream deliverables: documentation (implementation notes and SAP Help), training, and rollout materials. E-invoicing is the first focus area.

The specifications and curated knowledge this pipeline produces also ground SAP Business AI agents, so they can reason about and act on compliance topics precisely and stay current as regulations change.

• Design, build, and own the data-science and machine-learning solutions behind the pipeline — model selection, training, evaluation, and monitoring in production.
• Build and own evaluation and benchmarking frameworks that measure AI output quality, reliability, and model behavior, and the human-validation gates that keep legal and compliance experts in control of what the pipeline produces.
• Produce and curate the grounded knowledge that SAP Business AI agents rely on, so their compliance answers and actions stay accurate and traceable to source.
• Integrate advanced data capabilities — embeddings, semantic layers, and vector search — to keep datasets AI-ready, and lead data discovery, transformation, and modeling.
• Establish and improve data-quality frameworks: validation, monitoring, and observability through logs, metrics, and alerts.
• Reason rigorously about data quality, bias, and model behavior, and build ethical-AI, governance, and privacy practices into the systems you deliver.
• Partner with platform, data, AI, and consulting teams to deliver robust, production-ready solutions and continuously strengthen performance, scalability, and cost efficiency.

In the longer horizon, the team moves localization from code-based to configuration-based: legislation expressed as structured configuration rather than code, new localizations onboarded against a common platform, and compliance behavior that agents can extend and adapt — with a human always in the loop.

Where you belong

You'll take end-to-end technical ownership of critical systems and contribute to engineering excellence across the team. Mentoring less-experienced engineers, contributing to best practices, and building solid technical capabilities within the team are all part of the role. The team operates across global, cross-functional groups spanning data, AI, product, and platform engineering, in a culture that values continuous improvement, operational excellence, and knowledge sharing.

Skills used in this role

Applied statistics and machine learning, deep learning, foundation models, feature engineering, model training and monitoring, evaluations and benchmarking engineering, AI output quality assurance, semantic retrieval, embeddings and vector search, data preparation and validation, data pipeline development, data governance, ethical AI, PII handling, knowledge graph integration, context engineering, observability, troubleshooting, AI-assisted development, AI adoption capability.

Equal Employment Opportunity (EEO) Statement

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About SAP
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