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.
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.