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:
o Link Prediction
o Node Classification
o Recommendation Systems
o Network Analysis
o Knowledge Graph Analytics
o Fraud Detection
o 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:
o Graph Convolution Networks (GCN)
o Graph Attention Networks (GAT)
o GraphSAGE
o Heterogeneous Graph Networks
o 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.
