Data Scientist - Graph Neural Networks

• Graph Neural Networks (Non-Negotiable)
• Architecture used
• Deep Learning
• Neural network design

Location: US Remote

Contract: 12+ Months with possible extension

Experience: 7–12 of relevant years of experience

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.

• 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.

• 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.
• 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.
• Python & Advanced Machine Learning
• Strong experience with:
  • Python
  • Num Py
  • Pandas
  • Scikit-learn
  • Data processing and feature engineering
  • GNN Frameworks
• Hands-on expertise with:
  • PyTorch Geometric (PyG)
  • Deep Graph Library (DGL)
  • Tensor Flow GNN
  • Deep Learning
• Experience with:
  • Py Torch
  • Tensor Flow
  • Neural network design
  • Hyperparameter tuning
  • Model optimization
  • Graph Data Modeling
• Experience working with:
  • Node and edge feature engineering
  • Graph embeddings
  • Knowledge graphs
  • Graph representation learning
  • Communication & Stakeholder Management
• Ability to explain complex graph-based concepts to business stakeholders.
• Experience working in cross-functional delivery teams.

• Neo4j
• Tiger Graph
• Amazon Neptune
• Janus Graph
• NetworkX

• GraphRAG
• Knowledge Graphs
• LLM + Graph integrations
• Retrieval-Augmented Generation (RAG)
• Lang Chain / Lang Graph

• Azure Machine Learning
• AWS Sage Maker
• Google Vertex AI
• Docker
• Kubernetes
• CI/CD for ML

• Spark
• GraphX
• Databricks
• Snowflake
• Delta Lake

• Utilities
• Energy
• Power Systems
• Supply Chain
• Telecommunications
• Financial Services

• 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.

ApTask is a leading global provider of workforce solutions and talent acquisition services, dedicated to shaping the future of work. As an African American-owned and Veteran-owned company, ApTask offers a…