Research Data Scientist – Knowledge Graphs & AI Reasoning

Project overview

For our client, we are looking for an experienced Research Data Scientist for Knowledge Graphs and AI-supported Reasoning. You will develop and evaluate methods to build reliable knowledge graphs from unstructured data and enable AI applications to draw verifiable conclusions from these graphs.

Framework Conditions

• Planned period: 13.11.2026 to 30.06.2027
• Location: 100% remote
• Project scope: 1,300 hours
• Type of engagement: Freelance project

Responsibilities

• Develop metrics for the quality and completeness of Knowledge Graphs. Consider coverage, consistency, correctness, data provenance, and uncertainty.
• Compare Named Entity Recognition (NER), Entity Resolution, Relation Extraction, and LLM-based approaches for knowledge extraction from unstructured data. Evaluate methods for ontologies, domain models, and concept hierarchies.
• Analyze graph structure, connectivity, communities, and knowledge gaps using graph-theoretic, statistical, and probabilistic methods.
• Investigate traversal and retrieval strategies, such as hop limits, edge direction, weighting, relevance scoring, and pruning. Design workflows for multi-step reasoning that critically examine intermediate steps and verify results based on evidence in the graph.
• Explore Link Prediction and Graph Embeddings. Keep derived statements and verified facts separate in the graph.
• Create benchmarks, baselines, and ablation studies. Transfer results into reproducible experiments, evaluation frameworks, and concrete recommendations.

Requirements

Interested?

Send us your current profile, your earliest possible start date, and your net hourly rate via Freelancermap. Briefly describe a project where you have built, evaluated, or used Knowledge Graphs for AI-supported Reasoning.

• Sound knowledge of graph theory, linear algebra, probability theory, statistics, and optimization
• In-depth research experience with Knowledge Graphs, Information Extraction, Ontology Learning, Semantic Modeling, and graph-based Machine Learning
• Experience with LLMs and agent-based workflows for research experiments, as well as with validating results based on measurements and evidence
• Very good knowledge of data analysis and programming with Python, SQL, and graph query languages, as well as experience with tools for reproducible research
• Experience with experimental design, error analysis, uncertainty estimation, and sensitivity analyses

Project details

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