Position Overview
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 technical 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.
Should-Have Skills
Graph Ecosystem
Neo4j
TigerGraph
Amazon Neptune
JanusGraph
NetworkX
Advanced AI & GenAI
GraphRAG
Knowledge Graphs
LLM + Graph integrations
Retrieval-Augmented Generation (RAG)
LangChain / LangGraph
Cloud & MLOps
Azure Machine Learning
AWS SageMaker
Google Vertex AI
Docker
Kubernetes
CI/CD for ML
Big Data Technologies
Spark
GraphX
Databricks
Snowflake
Delta Lake
Domain Experience (Preferred)
Utilities
Energy
Power Systems
Supply Chain
Telecommunications
Financial Services
Mandatory Qualification Criteria
- Demonstrated experience building Graph Neural Network (GNN) or Graph Machine Learning solutions.
- Must provide at least one detailed Graph ML implementation example.
- Strong hands-on knowledge of graph modeling, graph embeddings, and GNN architectures.