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.
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.
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.