Title: Data Science- Graph Neural Networks (GNN) & Graph Machine Learning
Contract : 12 Months
Location: US
Working Model: Remote
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.