Data Science- Graph Neural Networks (GNN) & Graph Machine Learning

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.