Graph Intelligence / Data Visualization Specialist

About the Role

Deep Knowledge Group (https://www.dkv.global/) is seeking a senior specialist in graph intelligence, knowledge graphs and advanced network visualization to help turn large entity datasets into usable relationship intelligence.

Across DKG's analytical platforms, the important question is often not merely “what entities exist?” but:

How are they connected? Which relationships matter? Where are the clusters, central actors, hidden dependencies, emerging ecosystems and strategic gaps?

DKG maintains and develops complex datasets covering companies, investors, technologies, industries, experts, institutions, partnerships, geographical ecosystems and other interconnected entities. The objective of this role is to transform these relationships into scalable analytical systems and interactive intelligence products.
 

Key Responsibilities

• Design graph data models for complex multi-entity analytical datasets.
• Develop knowledge graphs and network-intelligence systems connecting companies, investors, technologies, people, institutions, projects, geographies and other entity types.
• Build pipelines for transforming structured and semi-structured datasets into graph-ready data.
• Work on entity normalization, relationship definitions and graph-schema design.
• Apply network-analysis methods including centrality, clustering, community detection, connectivity analysis and relationship mapping.
• Identify strategically relevant patterns and relationships that conventional tables and dashboards may not reveal.
• Develop interactive graph interfaces that allow users to explore networks without producing unreadable “hairball” visualizations.
• Create layered views, filters, drilldowns and contextual information that make large graphs operationally useful.
• Integrate graph analytics into DKG dashboards, ecosystem platforms and intelligence products.
• Work with data engineers and analysts to ensure graph outputs are analytically valid.
• Build reusable graph components and analytical frameworks for application across different DKG industries.
• Explore graph machine learning or predictive relationship analytics where these provide genuine practical value.
• Document graph schemas, relationships and analytical methodology.
   

Required Skills

• Strong practical experience with graph analytics, network science, knowledge graphs or closely related work.
• Strong Python skills.
• Experience with NetworkX, igraph or comparable graph-analysis libraries.
• Experience with graph databases such as Neo4j, Memgraph, ArangoDB or equivalent.
• Strong understanding of graph schemas, nodes, edges, properties and relationship modelling.
• Experience with advanced graph visualization tools or frameworks such as D3.js, Sigma.js, Graphistry, Gephi or similar.
• Experience cleaning and structuring complex real-world datasets.
• Ability to convert analytical requirements into appropriate graph structures and algorithms.
• Strong understanding of visual usability for large and complex networks.
• Ability to explain graph-derived findings in practical strategic terms.
• Strong English communication skills.
   

Strong Advantages

• Experience with graph machine learning, embeddings or link-prediction techniques.
• Experience with PyTorch Geometric, DGL or comparable graph-ML frameworks.
• Experience building market-intelligence, investment-intelligence, OSINT or ecosystem-mapping products.
• Experience with entity resolution and ontology/taxonomy design.
• Experience integrating graphs into production web applications.
• Familiarity with AI-assisted coding and analytical workflows.
• Exposure to investment analytics, AI, DeepTech, Life Sciences or other complex knowledge domains.
   

What Makes This Role Different

We are not looking for somebody whose main output is visually impressive network diagrams.

The purpose of graph intelligence at DKG is to expose relationships that improve actual analysis and decision-making—for example:

• identifying important companies or investors inside an ecosystem;
• mapping technology-to-company-to-investor relationships;
• identifying clusters and emerging technological concentrations;
• visualizing stakeholder and partnership ecosystems;
• tracing relationships between projects, organizations, markets and strategic opportunities.

The successful candidate must therefore combine graph engineering, analytical reasoning and visual-product design.
 

What Success Looks Like

A successful Graph Intelligence Specialist will create graph systems that become reusable infrastructure across DKG's analytical products: technically robust, analytically meaningful and sufficiently intuitive that users can extract useful insights without needing to understand graph theory.