Data Science Expert

What matters most

• Building Enterprise Semantic Infrastructure

  The core mandate is to design and maintain ontologies that ground AI agents in SAP's business reality. You must demonstrate the ability to harmonize disparate data sources like Salesforce, Workday, and ServiceNow into a unified semantic layer that enables accurate AI reasoning.

• Developing Production-Grade AI/ML Solutions

  You are expected to build and deploy RAG pipelines, vector databases, and LLM-based solutions that are reliable in production. This requires balancing advanced research-level techniques with the practical constraints of enterprise-scale software deployment and lifecycle management.

• Cross-Functional Stakeholder Collaboration

  Success depends on your ability to translate ambiguous business challenges from product, engineering, and customer-facing teams into concrete technical AI requirements. You must be able to communicate complex semantic and AI concepts to non-technical stakeholders effectively.

Likely challenges to think through

• Balancing RDF vs. Property Graph Architectures

  You will face the technical challenge of choosing between RDF triple stores and property graph databases for specific use cases. Prepare by articulating the trade-offs between these models regarding query performance, schema flexibility, and integration with existing SAP data structures.

• Grounding AI in Complex HCM Domain Semantics

  Working within the SuccessFactors domain requires mapping nuanced HR concepts—such as compensation, performance, and workforce planning—into formal knowledge representations. Prepare by reviewing how to translate business-level policies and terminology into machine-readable semantic models.

Topics to prepare

• Enterprise Knowledge Engineering

  Review the principles of designing ontologies and semantic models for complex business landscapes. Exercise: Map a sample business process (e.g., Procure-to-Pay) into a formal ontology structure using W3C standards like OWL or RDF. This is critical for the role's focus on grounding AI agents in SAP's business ontology.

• Agentic AI and Reasoning Frameworks

  Study architectures for autonomous agents, including planning, orchestration, and tool use. Exercise: Design a conceptual multi-agent architecture that could handle a specific HCM task, such as workforce planning, by integrating data from multiple sources. This aligns with the preferred qualification for building autonomous agents.

• Semantic Retrieval and RAG Pipelines

  Deepen understanding of how to combine vector databases with knowledge graphs for enterprise-grade RAG. Exercise: Compare the retrieval accuracy of a pure vector-based RAG system versus a graph-augmented RAG system using a sample dataset. This is essential for the requirement to build reliable, grounded AI solutions.

Skills to revise

• Graph Query Languages

  Practice writing complex queries in SPARQL, Cypher, or GQL. Exercise: Write a query to traverse relationships between disparate entities (e.g., employees, skills, and compensation data) in a property graph database. This directly addresses the hands-on requirement for semantic data systems.

• Production-Grade Python and ML Libraries

  Refine skills in building and deploying ML models using PyTorch or TensorFlow. Exercise: Create a modular pipeline that processes raw data, performs an inference task, and includes logging and error handling for production environments. This is necessary for the proven track record requirement in deploying AI/ML solutions.

• Cloud and Big Data Infrastructure

  Gain familiarity with managing AI workflows on platforms like Databricks and major cloud providers (AWS/Azure/GCP). Exercise: Outline a scalable architecture for deploying a GenAI model that consumes data from a cloud-based data lake. This supports the requirement for operating AI solutions at scale.

• Architect a Knowledge Graph Strategy

  Prepare a technical summary comparing RDF triple stores and property graph databases. Be ready to discuss how you would design an enterprise ontology to harmonize disparate data sources like SAP, Salesforce, and Workday into a unified semantic layer.

• Develop a RAG Pipeline Walkthrough

  Create a high-level architecture diagram of a RAG pipeline that incorporates enterprise knowledge grounding. Explain how you would handle data retrieval, embedding selection, and vector database management to ensure AI agents provide accurate, context-aware responses.

• Prepare Case Studies on Production AI

  Select two past projects where you deployed ML or GenAI solutions into production. Focus on the full lifecycle, including data ingestion, model evaluation, and how you addressed challenges in scalability, maintenance, and continuous improvement.

• Review SAP Domain and Integration Technologies

  Research SAP's ecosystem, specifically the SAP One Domain Model, SAP Datasphere, and OData APIs. Prepare to discuss how you would map complex business processes like Order-to-Cash or HCM workflows into semantic models for agentic AI.

• Formulate Strategic Questions for the Team

  Prepare questions regarding the current maturity of SAP's knowledge graph infrastructure, the specific reasoning frameworks used for multi-agent architectures, and how the team balances model accuracy with the security requirements of enterprise business data.