Data Scientist - Graph Neural Networks (GNN)

Role Type: Data Scientist – Graph AI / Graph Machine Learning Location: US Remote Employment Type: C2C Contract : 12+ Months Position Overview 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. Must-Have Skills (Mandatory) 1. Graph Neural Networks (Non-Negotiable) 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.