Title: Data Scientist - Graph Neural Networks (GNN) & Graph Machine Learning
Contract Duration: 12+ months/ Long Term
Working Model: Remote
Must Have Skills: Prior Experience in GNN/Graph ML implementation experience is mandatory.
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
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.
