NetworkX Experts in Germany
matched in minutes from over 15,000 CVs with the power of AI.Hire experts who turn graph ideas into working Python solutions: network analysis, shortest-path and centrality logic, graph data preparation, and clean handover for research or production work. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used NetworkX
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Louis Guitton
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
- Work with customers to understand their challenges and provide the best solutions based on open-source data products
- Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
- Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
- Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
- Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
- Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
- Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible
Moritz Kath
Last position:
Senior DevOps Engineer GCP at tedi GmbH & Co. KG
- Design and implementation of DevOps and CI/CD practices for data and analytics teams
- Introduction of infrastructure as code with Terraform (IaC)
- Setup and maintenance of GCP user and permission management with Terraform in multi-project environment
- Design and implementation of CI/CD pipelines with GitHub
- Leading and training developer team for the introduction of IaC and CI/CD practices
- Building and optimising database connectors with Apache Arrow for terabyte scale data extraction (Oracle, SAP)
- Optimising data lake storage and warehouse ingest
Robin Steinkühler
Last position:
Consultant, Data Science & Engineering at valantic Digital Finance GmbH
- Bridged business and engineering for enterprise finance clients, designing data products and cloud pipelines in Python, SQL Server, SAP Datasphere, and Tagetik
- Conceived, built, and containerised a Python/FastAPI universal connector that syncs SAP S/4HANA and other SQL/NoSQL sources to Tagetik, deployed on Google Cloud Run and Microsoft Azure, cutting a critical 90-minute data load to approximately 80 seconds (65× faster)
- Architected a medallion-layer SQL Server warehouse ingesting approximately 500 GB/day from 11 ERP instances, automating daily refreshes (full load under 6 minutes) and freeing 20–30 finance staff from days of manual data consolidation
- Led cross-functional workshops to design enterprise EPM target architecture for a leading Southeast-Asian telecom (CAPEX, OPEX, revenue), translating requirements into data-model specifications and integration blueprints now being built by the client’s implementation team
- Delivered selected projects including a consolidated data & reporting warehouse for a global manufacturer (10 k+ employees), NFI reporting for an international management & technology consultancy, and CAPEX/OPEX planning for a Southeast-Asian telecom (20 k+ employees)
Evangelia Charvati
Last position:
Postdoctoral Researcher & Data Steward at Technical University Darmstadt (Müller-Plathe group)
- Developed and applied a symbolic regression scheme to explore and model complex relationships in liquid viscosities across their phase space, providing predictive insights into material properties.
- Contributed to the improvement of hybrid Particle-Field models through data-driven diagnostics and model enhancement strategies.
- Lead interdisciplinary collaboration with experimental physicists for the investigation of the molecular mechanisms behind water-based inks.
- Managed research data workflows to ensure public accessibility and institutional archiving of simulation data, code scripts, and inputs, directly supporting research transparency and reproducibility.
Giovanni Spinelli Barrile
Last position:
Technical Product Manager at Logicc GmbH
Acted as the primary bridge between Legal, Engineering, and Business units to ensure zero compliance violations while maintaining product velocity.
Led the development of a GDPR-compliant AI aggregator platform, managing a roadmap that balances legal constraints with aggressive feature delivery.
Scaled the engineering team from 4 to 9 developers, establishing hiring protocols and technical onboarding processes to support rapid product iteration.
Boosted the development process by introducing structured sprint cycles and backlog refinement, resulting in a 20% reduction in feature delivery time.
Architected and prototyped agentic AI workflows with n8n and RAG pipelines on Langchain.
Sagar Mattikere Anand
Last position:
Graph-Based RAG Agent for Secure Data Intelligence (EcoGraph-RAG) at Philipps University Marburg
- Designed GraphRAG system combining semantic vectors (Chroma) + knowledge graphs (NetworkX/Neo4j) for multi-hop Q&A on climate policy docs.
- Deployed Llama 3/Gemma via Ollama for $0-cost local inference; achieved ~95% entity-relation extraction accuracy.
- Built ingestion pipeline for PDFs + 48k-row CSVs; applied grouped median imputation and fixed data sparsity.
Maria Daniela Leite De Souza
Last position:
Research Scientist at Intel Labs
- Designed and implemented AI-driven orchestration models for large-scale distributed systems.
- Developed Python and Go-based optimization pipelines to automate service placement across Kubernetes clusters.
- Worked on scalable data pipelines and production-ready model integration with serverless frameworks (intent-driven K8s operators).
- Contributed to the Horizon Europe VERGE project.
Manasvi Kumar
Last position:
Max-Cut with MQT-Quantum Auto Optimizer (QAOA vs Classical Baseline) at Independent Project
- Formulated Max-Cut as a QUBO and implemented QAOA vs a Simulated Annealing baseline with reproducible parameter sweeps, fixed seeds, and exact checks on small graphs
- Verified both approaches reached the known optimum on all tested instances; deeper QAOA increased runtime with limited gains on these cases
- Tech: Python · mqt.qao · Qiskit · qiskit-algorithms · NetworkX · Matplotlib · NumPy · SymPy
Raksha Shet
Last position:
Working Student – Industrial Foundation Model at Siemens AG
- Design and implement an end-to-end Siemens NX based pipeline to convert OBJ CAD models into graph representations by applying AI-driven clustering of mesh faces into nodes and face adjacency for edges, streamlining GNN integration
- Generate a large-scale synthetic 3D CAD dataset, annotating parts with few MFCAD-style features to ensure balanced, diverse training data for GNN workflows
- Support the design, training, and evaluation of graph neural network architectures for AI-driven detection and classification of geometric features in 3D CAD shapes, accelerating feature-recognition workflows
Discover over 15,000 top freelancers
Statistics of experts using NetworkX
Aggregated from the professional profiles of matched freelancers.
Experience
8 years
Position duration
1.6 years
Positions per freelancer
7
Top business areas
Information Technology, Research and Development, Business Intelligence
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
90%
Doctorate
30%
Certifications per freelancer
5
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using NetworkX
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Graph work
NetworkX is a Python library for building and studying graphs. Companies use it to model relationships, routes, dependencies, and flows in data.
Typical work includes:
- graph creation and cleanup
- path, ranking, and community analysis
- prototype support for research teams
- integration with pandas, NumPy, and Python data tools
Where it fits
It is a strong fit when data is connected, not flat. That includes logistics maps, social graphs, fraud links, supply chains, knowledge graphs, and dependency checks in software or infrastructure.
A good specialist knows when NetworkX is the right tool and when a faster graph database or a different analytics stack is better. That judgment saves time and keeps the work simple.
Ecosystem skills
Strong professionals working with NetworkX usually bring solid Python skills and comfortable work with data pipelines. They often use Jupyter notebooks, pandas, Matplotlib, SciPy, and graph file formats such as GraphML or edge lists.
They should also understand:
- graph types and directionality
- network measures and traversal
- data cleaning for sparse or messy relations
- handoff to dashboards, research notes, or APIs
When to bring help
Companies bring in freelance specialists when a graph task is urgent, unusual, or tied to a one-off decision. That happens during feasibility studies, analytics spikes, internal tools, and model validation.
In Germany, remote work is common for this kind of Python graph analysis. On-site work can help when teams need close collaboration with local data owners, analysts, or product specialists.
What strong experts do
Good NetworkX professionals do more than run library functions. They ask where the data comes from, how the graph will be used, and which outputs matter to the business.
They write code that is easy to review, test, and reuse. They also explain trade-offs clearly, especially when a project may outgrow NetworkX and need a different graph stack.
Choosing the right fit
Look for specialists who can show real graph work, not just general Python scripts. Strong signs include clear reasoning about node and edge design, robust cleaning steps, and practical choices for performance.
If the work touches research, operations, or decision support, the best expert will connect the graph logic to the end use. That makes the result easier to trust and easier to maintain.
Frequently asked questions
Before you brief your next project: the most common questions about NetworkX.
NetworkX is used to model and analyze connected data in Python. Companies use it for routing, dependency maps, social or fraud graphs, knowledge relationships, and network-style research. It is especially useful when the work needs flexible analysis rather than a full graph database setup.
NetworkX is a Python library, so it is best for analysis, prototyping, and custom graph logic inside code. A graph database is usually better for large, persistent, multi-user graph storage, while libraries like igraph or graph-tool may be faster for some heavy workloads. The right choice depends on scale, data access, and how the graph will be used.
A strong NetworkX specialist should also know Python well, especially pandas and NumPy. Jupyter, data cleaning, plotting, and basic statistics are common needs too. If the project is business-facing, clear documentation and the ability to explain graph measures matter just as much.
A small NetworkX task may only need a specialist who can clean graph data and produce the needed analysis. More complex work benefits from someone who understands graph design, performance trade-offs, and how the results will be used downstream. If the work supports a product or decision process, look for proven delivery rather than just theory.
Yes, most NetworkX work can be done remotely because it lives in Python code and data files. For teams in Germany, remote collaboration is often enough when data access, reviews, and feedback are well organized. On-site time can help if the graph depends on local context or sensitive internal data.
A good NetworkX expert explains the graph model clearly before writing code. Look for clean node and edge definitions, sensible choices for graph algorithms, and results that match the business question. Strong specialists also mention limits, such as memory use or when another tool would fit better.
A NetworkX project often ends with analysis notebooks, reusable Python functions, cleaned graph data, and clear written findings. Some specialists also deliver plots, exports for other systems, or a small internal tool. The best deliverable is one your team can reuse without guessing how the graph was built.
NetworkX can work in production for smaller services, internal tooling, and graph logic that is not too heavy. For very large graphs or high-throughput applications, a specialist may recommend a different stack. A good freelancer will tell you early when NetworkX is a fit and when it is only part of the solution.
The average hourly rate of freelancers in Germany who have used NetworkX in their recent projects is 93 €, which corresponds to a daily rate of about 743 € based on an 8-hour working day.
Of the freelancers in Germany who have used NetworkX in their recent projects, 100% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 30% hold a doctorate.
On average, freelancers in Germany who have used NetworkX in their recent projects have 8 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Germany who have used NetworkX in their recent projects are German (100%), English (100%), and French (40%).
The most common industries among freelancers in Germany who have used NetworkX in their recent projects are Information Technology (90%), Education (70%), and Professional Services (40%).
The most common business areas among freelancers in Germany who have used NetworkX in their recent projects are Information Technology (80%), Research and Development (80%), and Business Intelligence (70%).
Main locations of FRATCH Experts, who have recently used NetworkX
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
