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# Data Scientist - Graph Neural Networks (GNN)

- **Company:** Gerardo Noriega Architect, LLC
- **Location:** United States (Remote available)
- **Contract:** Contract
- **Skills:** LangGraph Framework, Amazon Web Services, Data Analysis, Artificial Neural Networks, Big Data, Network Analysis, Cloud Computing, Software Code Optimization, Continuous Integration, Fraud Prevention and Detection, Graph Database, Python (Programming Language), Machine Learning, Neo4j, Node.Js, NumPy, Performance Tuning, Recommender Systems, Tensorflow, Azure Machine Learning, TigerGraph, Data Processing, Feature Engineering, Graph Neural Networks, Pytorch, Large Language Models, Snowflake, Apache Spark, Google Vertex AI, Deep Learning, Model Validation, Generative AI, Pandas, Data Lakes, Scikit Learn, Kubernetes, Machine Learning Operations, Feature Extraction, Entity Resolution, Graph RAG, Data Pipelines, Docker, Databricks
- **Published:** October 27, 2026
- **Apply:** https://www.dice.com/job-detail/3315467d-38ff-4d0f-8126-3314d0151eb4

## About the Role

Proven hands-on experience implementing:

 * Graph Convolution Networks (GCN)
 * Graph Attention Networks (GAT)
 * GraphSAGE
 * Heterogeneous Graph Networks
 * Temporal GNNs

Experience solving real-world Graph ML problems.

 2. Demonstrated Graph ML Delivery Experience

Candidate must provide examples of prior graph-based machine learning implementations, including:

 * Problem statement
 * Graph modeling approach
 * Architecture used
 * Business outcome achieved

Note: Prior experience in power systems is not mandatory. However, prior Graph ML/GNN implementation experience is mandatory.

 3. Python & Advanced Machine Learning

Strong experience with:

 * Python
 * NumPy
 * Pandas
 * Scikit-learn
 * Data processing and feature engineering

 4. GNN Frameworks

Hands-on expertise with:

 * PyTorch Geometric (PyG)
 * Deep Graph Library (DGL)
 * TensorFlow GNN

 5. Deep Learning

Experience with:

 * PyTorch
 * TensorFlow
 * Neural network design
 * Hyperparameter tuning
 * Model optimization

 6. Graph Data Modeling

Experience working with:

 * Node and edge feature engineering
 * Graph embeddings
 * Knowledge graphs
 * Graph representation learning

 7. Communication & Stakeholder Management

Ability to explain complex graph-based concepts to business stakeholders.

Experience working in cross-functional delivery teams.

Should-Have Skills

Graph Ecosystem

 * Neo4j
 * TigerGraph
 * Amazon Neptune
 * JanusGraph
 * NetworkX

Advanced AI & GenAI

 * GraphRAG
 * Knowledge Graphs
 * LLM + Graph integrations
 * Retrieval-Augmented Generation (RAG)
 * LangChain / LangGraph

Cloud & MLOps

 * Azure Machine Learning
 * AWS SageMaker
 * Google Vertex AI
 * Docker
 * Kubernetes
 * CI/CD for ML

Big Data Technologies

 * Spark
 * GraphX
 * Databricks
 * Snowflake
 * Delta Lake

Domain Experience (Preferred)

 * Utilities
 * Energy
 * Power Systems
 * Supply Chain
 * Telecommunications
 * Financial Services

Mandatory Qualification Criteria

 * Demonstrated experience building Graph Neural Network (GNN) or Graph Machine Learning solutions.
 * Must provide at least one detailed Graph ML implementation example.
 * Strong hands-on knowledge of graph modeling, graph embeddings, and GNN architectures.

## Description

We are seeking a highly skilled Data Scientist with proven expertise in Graph Neural Networks (GNNs) and Graph Machine Learning to lead the design, development, and implementation of graph-based AI models as part of a strategic Proof of Concept (POC). The GNN architecture is the technical core of this engagement and, therefore, candidates must demonstrate prior hands-on experience building, training, evaluating, and deploying graph-based machine learning solutions.

General Data Science, Machine Learning, or Deep Learning experience alone will not be considered sufficient.

Key Responsibilities

 * Design, build, and optimize Graph Neural Network (GNN) models for complex business problems.
 * Develop graph-based solutions for:

 * Link Prediction
 * Node Classification
 * Recommendation Systems
 * Network Analysis
 * Knowledge Graph Analytics
 * Fraud Detection
 * Entity Resolution

 * Build scalable graph data pipelines and feature engineering workflows.
 * Work with large-scale graph datasets and graph databases.
 * Conduct model evaluation, experimentation, and performance optimization.
 * Collaborate with domain experts, architects, and engineering teams to deliver production-ready solutions.
 * Present technical findings and solution recommendations to stakeholders.