Senior Research Data Scientist - Knowledge Graphs & AI

Project overview

Research, conception, and evaluation of innovative methods for the creation, further development, and utilization of high-quality Knowledge Graphs for AI applications. Development and implementation of procedures for assessing the quality and completeness of Knowledge Graphs regarding coverage, consistency, accuracy, data provenance, and uncertainties. Analysis and benchmarking of various AI-supported methods for extracting and structuring information from unstructured data, particularly Named Entity Recognition (NER), Entity Resolution, Relation Extraction, and Large Language Models (LLMs).

Evaluation and further development of ontologies, domain models, semantic data models, and concept hierarchies. Application of graph-theoretical, statistical, and probabilistic methods for analyzing graph structures, relationships, networks, clusters, and information gaps. Investigation and optimization of graph traversal and retrieval strategies, for example, regarding hop limits, edge direction, weighting, relevance assessment, and data reduction. Conception and development of multi-stage AI-supported reasoning, validation, and verification processes based on Knowledge Graphs.

Research and evaluation of link prediction methods and graph embeddings with particular consideration of the separation between verified knowledge and derived information. Building benchmarks, reference models, and evaluation procedures, as well as conducting experiments, error analyses, and comparative studies. Transferring research results into reproducible experiments, evaluation frameworks, and practice-oriented recommendations.

Requirements

Completed studies in Computer Science, Data Science, Mathematics, Artificial Intelligence, or a comparable field, ideally with a scientific or research-oriented background. Profound mathematical knowledge, especially in graph theory, linear algebra, probability theory, statistics, and optimization. In-depth knowledge and practical experience in the areas of Knowledge Graphs, Information Extraction, Ontology Learning, semantic modeling, and graph-based machine learning.

Experience in the conception, development, and evaluation of AI and LLM-based solutions, as well as in the use of agent-based workflows to support research and analysis processes. Very good programming and data analysis skills, especially in Python and SQL, and ideally in graph query languages and corresponding technologies. Experience with experimental design, benchmarking, reproducible research processes, error analyses, and the evaluation of uncertainties and sensitivities. Strong analytical and conceptual skills, as well as a structured, scientifically sound, and results-oriented approach.

Ability to prepare complex research results understandably and transfer them into concrete technical concepts and recommendations. Very good communication skills and enjoyment of interdisciplinary collaboration between Data Science, AI Research, and IT Architecture.

Remote, from now until 07/31/2027.

Project details

• Start: flexible
• Remote: remote
• Location: DE
• Languages: German