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
- 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.