Data Science Senior Manager

About SAP Business Network

SAP Business Network connects millions of companies worldwide, enabling intelligent collaboration across procurement, supply chain, logistics, and financial processes. The organization is on a mission to make this network self-optimizing — leveraging Connected Intelligence to create business outcomes that no single company could achieve alone.

People Leadership

As a people manager, you are responsible for supporting the success of not only your direct reports, but the success of all employees within the larger team by helping to identify development opportunities and supporting team members to achieve their goals. You are expected to know about the members of your extended team and share insights with your peer managers.

• Act as a role model by coaching and recognizing direct reports, providing just-in-time feedback and expecting the same of your direct reports.

• Lead multiple Data and AI teams on-site at NPB, fostering a strong in-office culture of collaboration, engagement, and continuous growth.

• Build and scale new AI-native teams within the Business Network Core organization — hiring top talent and establishing best practices for AI-first development.

• Actively manage organizational change and enable people managers in own area of responsibility.

• Realize skills gaps well in advance and drive execution of up-skilling strategy; take appropriate steps to ensure retention in critical phases.

• Create an environment of continuous learning and improvement; regularly offer feedback to help others develop and support them in setting challenging development goals.

Strategic Direction

• Manage the Data and AI unit by continuously adapting its focus and execution capabilities to customer/market needs, company strategy, and financial objectives.

• Function as the operational link between the company's overall strategy and achieving business results; set direction through a clear vision, strategy, and business plan.

• Build execution plans with the management team and monitor execution against the business plan; manage critical resource situations and their impact on deliverables.

• Maintain in-depth knowledge of trends in Data Science and AI and translate those into strategy for SBN.

• Report regularly to senior executive steering committees and advise on strategic Data and AI topics.

• Act as role model and spokesperson for SAP inside and outside the organization.

Data Foundation & AI-Native Development

Transitioning into the SAP Business Network space, this role is central to building a comprehensive Data Foundation and advanced Analytics capabilities that support a unified ecosystem leveraging Connected Intelligence and a sophisticated Reasoning Engine to automate complex business logic across the network. Technical skills from SBN are rapidly converging as configuration logic and forecasting models evolve into the primary inputs for autonomous reasoning — transforming traditional software development into a specialized discipline of AI-driven data orchestration and intelligent systems.

• Define and execute the Data Foundation strategy for SBN, enabling a unified data ecosystem that supports Connected Intelligence and autonomous business logic.

• Lead the design and development of Reasoning Engine components that automate complex business processes across the Business Network.

• Align Data and AI capabilities with the converging technical landscape of SBN — translating configuration logic and forecasting models into primary inputs for autonomous AI reasoning systems.

• Oversee development and deployment of advanced ML and AI models including forecasting, recommendation, classification, and generative AI applications.

• Architect AI-driven data orchestration pipelines that serve the Business Network's operational and analytical needs at scale.

• Establish model governance, MLOps practices, and quality standards across all AI/ML workstreams.

• Partner with SBN Product, Engineering, and Architecture leadership to embed intelligent data systems into the core product roadmap.

Applied Data & AI Science

• Design and implement mathematical models, algorithms, and data mining processes into products, prototypes, and/or business processes to solve business problems.

• Analyze large and complex datasets to identify trends, patterns, opportunities, and areas for improvement.

• Develop and apply machine learning and artificial intelligence techniques to derive actionable insights.

• Present findings and recommendations to cross-functional stakeholders to inform strategic decisions.

What You Will Build

• A Data Foundation powering the entire SBN ecosystem with reliable, governed, high-quality data.

• AI-native teams — net-new squads building the next generation of intelligent capabilities for the Business Network Core.

• A Reasoning Engine automating complex business logic and workflow decisions across SAP's global supplier and buyer network.

• Forecasting and configuration models serving as primary inputs to autonomous AI decision systems.

• A collaborative, high-trust team culture at NPB that makes this one of the most sought-after Data and AI organizations in SAP.

Qualifications & Skills

Minimum Requirements

• Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related quantitative field.

• 8+ years of progressive experience in Data Science, Machine Learning, or related technical fields.

• 3+ years of experience managing and leading technical teams, including senior individual contributors and team leads.

• Proven track record of delivering large-scale ML/AI solutions in production environments.

Preferred Qualifications

• Master's or PhD in a quantitative discipline.

• Experience in B2B SaaS or enterprise software, ideally within the SAP Business Network domain.

• Hands-on experience with LLMs, generative AI, reasoning systems, or agentic AI architectures.

• Deep expertise in data orchestration platforms, feature engineering at scale, and MLOps tooling.

• Prior experience building or transforming teams in a high-growth or reorganization context.

• Familiarity with SAP BTP, SAP AI Core, or SAP AI Foundation.

Technical Skills

• Advanced ML & AI: solid understanding of supervised/unsupervised learning, time series forecasting, NLP, and deep learning concepts — sufficient to evaluate, challenge, and guide team output.

• Generative AI & LLMs: familiarity with prompt engineering, fine-tuning, RAG architectures, and agentic AI frameworks.

• Data platforms: working knowledge of Databricks, Snowflake, SAP Datasphere, or equivalent cloud-native data platforms.

• Data architecture: understanding of lakehouse patterns, real-time streaming (Kafka, Spark), and data mesh principles — sufficient to set direction and make architectural decisions.

• MLOps: awareness of experiment tracking (e.g. MLflow), model serving, CI/CD for ML pipelines, and drift monitoring — to govern standards across teams.

• Programming: Python (primary), SQL — sufficient for technical discussions, design reviews, and evaluating team work.

• Cloud: AWS, GCP, or Azure in enterprise/multi-cloud contexts.