Data Science-Graph ML & Graph Neural Networks (W2 Only)

Job 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 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 and 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.