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Описание: |
Deep Knowledge Group (www.dkv.global) is looking for a senior specialist in graph intelligence, knowledge graphs and network analysis to turn large entity datasets into usable relationship intelligence.
Across our analytical platforms the important question is usually not “what entities exist” but: how are they connected, which relationships matter, where are the clusters, central actors, hidden dependencies and emerging ecosystems.
We maintain datasets covering companies, investors, technologies, industries, experts, institutions, partnerships and geographical ecosystems. Your job is to turn those relationships into scalable analytical systems and interactive intelligence products.
Requirments:
* Graph Intelligence / Knowledge Graph Specialist (Neo4j, Python)
* Experience 5+ years
* English Upper-Intermediate
* Skill: Python, Neo4j, NetworkX, Knowledge Graphs, Graph Databases, Cypher, Network Analysis, Entity Resolution, D3.js
What you will do
* Design graph data models for complex multi-entity analytical datasets.
* Build knowledge graphs connecting companies, investors, technologies, people, institutions, projects and geographies.
* Build pipelines that turn structured and semi-structured data into graph-ready form.
* Work on entity normalization, relationship definitions and graph-schema design.
* Apply network analysis: centrality, clustering, community detection, connectivity, relationship mapping.
* Surface patterns that conventional tables and dashboards do not reveal.
* Build interactive graph interfaces that stay readable — layered views, filters, drilldowns, context — instead of hairballs.
* Integrate graph analytics into DKG dashboards and ecosystem platforms.
* Build reusable graph components applicable across different industries.
* Explore graph ML or link prediction where it delivers real practical value.
* Document schemas, relationships and methodology.
What we expect
* Strong practical experience with graph analytics, network science or knowledge graphs.
* Strong Python.
* NetworkX, igraph or comparable graph-analysis libraries.
* Graph databases: Neo4j, Memgraph, ArangoDB or equivalent.
* Solid grasp of graph schemas — nodes, edges, properties, relationship modelling.
* Graph visualization tools: D3.js, Sigma.js, Graphistry, Gephi or similar.
* Experience cleaning and structuring messy real-world data.
* Ability to translate an analytical requirement into the right graph structure and algorithm.
* Ability to explain graph-derived findings in practical strategic terms.
* English sufficient for daily written and spoken work in an international team.
Strong advantages
* Graph ML, embeddings, link prediction; PyTorch Geometric, DGL or comparable.
* Market-intelligence, investment-intelligence, OSINT or ecosystem-mapping products.
* Entity resolution, ontology and taxonomy design.
* Integrating graphs into production web applications.
* AI-assisted coding and analytical workflows.
* Exposure to investment analytics, AI, DeepTech or Life Sciences.
What makes this role different
We are not looking for someone whose main output is visually impressive network diagrams. Graph intelligence here exists to expose relationships that improve real analysis and decisions: finding the important companies and investors inside an ecosystem, mapping technology-to-company-to-investor links, detecting clusters and emerging technological concentrations, tracing relationships between projects, organizations, markets and opportunities.
Success means graph systems that become reusable infrastructure across DKG’s analytical products — technically robust, analytically meaningful, and intuitive enough that users get value without knowing graph theory.
Format: full-time, fully remote, international team, frontier-technology domains.
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