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)
- Graph Neural Networks (Non-Negotiable)
- Proven hands-on experience implementing:
Graph Convolution Networks (GCN)
Graph Attention Networks (GAT)
Graph SAGE
Heterogeneous Graph Networks
Temporal GNNs
- Experience solving real-world Graph ML problems.
Demonstrated Graph ML Delivery Experience
Candidate must provide examples of prior graph-based machine learning implementations, including:
- Problem statement
- Graph modelling approach
- Architecture used
- Business outcome achieved
Note: Prior experience in power systems is not mandatory. However, prior Graph ML/GNN implementation experience is mandatory.
- Python & Advanced Machine Learning
- Strong experience with:
Python
NumPy
Pandas
Scikit-learn
Data processing and feature engineering
- GNN Frameworks
- Hands-on expertise with:
PyTorch Geometric (PyG)
Deep Graph Library (DGL)
TensorFlow GNN
- Deep Learning
- Experience with:
PyTorch
TensorFlow
Neural network design
Hyperparameter tuning
Model optimization
- Graph Data Modeling
- Experience working with:
Node and edge feature engineering
Graph embeddings
Knowledge graphs
Graph representation learning
- Communication & Stakeholder Management
- Ability to explain complex graph-based concepts to business stakeholders.
- Experience working in cross-functional delivery teams.