Data Scientist Jobs

About The Position

Requirements

• Typically 5 or more years of hands-on data science, machine learning, or applied research experience taking projects from an open question to a measurable result, or a PhD plus 2 or more years of experience.
• MS or PhD in computer science, machine learning, statistics, mathematics, or a related field, or equivalent practical experience.
• Hands-on depth in at least one of: knowledge graphs, ontologies or semantic layers; entity resolution, schema matching or data integration; machine learning on relational, graph or tabular data, including GNNs; or LLM systems over structured data such as text-to-SQL, schema linking, API discovery, GraphRAG, agents, and evaluation.
• Strong foundations in machine learning and statistics, with disciplined experimentation including strong baselines, ablations, leakage-safe splits, error analysis, and honest reporting.
• Strong Python and SQL skills and experience with PyTorch or a similar framework; comfort working with large, messy, multi-table data.
• Strong problem-solving skills, demonstrated by turning a vague question into an experiment that produces a clear answer within weeks.
• Strong written and verbal communication skills, including the ability to clearly explain negative or mixed results and communicate work to scientists or executives.

Nice To Haves

• Publications at venues such as NeurIPS, ICML, ICLR, KDD, ACL/EMNLP, SIGMOD/VLDB, ISWC, or WWW, or contributions to open-source projects.
• Experience with graph databases, vector search, or large-scale data processing such as Spark.
• Experience with LLM-assisted ontology learning or knowledge-graph construction.
• Experience with LLM fine-tuning or post-training, or MCP.
• Experience with process mining, causal inference, or forecasting, and familiarity with enterprise processes such as finance, procurement, or supply chain.
• SAP experience.

Responsibilities

• Own research threads from ambiguous questions through methods, experiments, reproducible results, and decisions about next steps.
• Reproduce promising research papers on SAP data and recommend which approaches merit further investment.
• Develop methods to learn and maintain ontologies of business objects, processes, rules, and actions, extending SAP semantic models with techniques such as LLMs, schema matching, and entity resolution.
• Develop agents grounded in business context, including graph-augmented retrieval and text-to-query systems over large schemas, with checks for business rules, access rights, and governed actions.
• Apply graph neural networks and relational foundation models with knowledge-graph context to prediction, anomaly detection, and what-if analysis.
• Create datasets, benchmarks, and evaluation harnesses for realistic enterprise data, using synthetic or privacy-preserving data where real data cannot be shared.
• Write clean Python and build prototypes with product and engineering teams for potential adoption.
• Publish research papers or open-source releases and mentor interns.
• Collaborate with universities and research institutes, including Stanford HAI.
• In the first year, own a hands-on research thread through a benchmark and a product-team-adopted prototype, and work toward a paper submission or open-source release.

Benefits

• Great benefits (details and eligibility requirements are referenced on SAP North America Benefits).
• Flexible working models.
• Health and well-being support.
• Constant learning and skill growth.