Data Scientist - Graph Neural Networks & Graph ML (W2 Only)

Title: Data Scientist- Graph Neural Networks (GNN) & Graph Machine Learning

Contract Duration: 12+ months/ Long Term

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.

Note: Prior experience in power systems is not mandatory. However, prior Graph ML/GNN implementation experience is mandatory. 

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

3. Python & Advanced Machine Learning - Strong experience with: Python, NumPy, Pandas, Scikit-learn and 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.