Senior Research Data Scientist – Knowledge Graphs & Generative AI

Responsibilities

The profile sought:

• 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-based 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-based 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 for action.
• 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, particularly 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, particularly 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 to work.
• Ability to present complex research results understandably and to translate them into concrete technical concepts and recommendations for action.
• Very good communication skills and enjoyment of interdisciplinary collaboration between Data Science, AI Research, and IT Architecture.

Project details

• Start: 2026-11
• Duration: 8 Monate
• Remote: remote
• Location: Remote, Deutschland, DE
• Languages: German