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Data Science- Graph Neural Networks (GNN) & Graph Machine Learning

ApTaskUnited States🇺🇸United StatesPosted Sep 11, 2026

Quick Overview

Salary
$60/hr
Seniority
Mid Senior
Work mode
Remote
Location
United States
Posted
6 days ago
Machine LearningNumPyScikit-learnDeep LearningPandasPyTorchPythonStakeholder ManagementTensorFlow

Job Description

 
Hi,
Greetings from ApTask!

My name is Surya; We are currently looking for a Data Science- Graph Neural Networks (GNN) & Graph Machine Learning  for an exciting opportunity.

If you are interested and your experience aligns with this role, please share your updated resume for further discussion
 
 
Job title: Data Science- Graph Neural Networks (GNN) & Graph Machine Learning 
Location: Remote
Contract
 
Immediate interview 
 
Rate =$60/hr on w2 (NO C2C Under employer)
 
 

 Must Have skills: Graph Neural Networks (GNN) & Graph Machine Learning, Python

 

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.

 

 

Surya S

Sr. Recruiter (Sales & Leadership Hiring)

Phone:

 

 

ApTask | A global, diversity-certified workforce solutions provider.

Address: 120 Wood Ave South, Suite # 300, Iselin, NJ 08830

 

This e-mail and any attachments may be confidential, proprietary or legally privileged. Any review, use, disclosure, distribution or copying of this e-mail is prohibited except by or on behalf of the intended recipient. If you received this message in error or are not the intended recipient, please delete or destroy the e-mail message and any attachments or copies and notify the sender of the erroneous delivery by return e-mail. It shall not attach any liability on the sender or ApTask or its affiliates. Any views or opinions presented in this email are solely those of the sender and may not necessarily reflect the opinions of ApTask or its affiliates.

 

Applicant Consent:
By submitting your application, you agree to ApTask's ()  and , and provide your consent to receive SMS and voice call communications regarding employment opportunities that match your resume and qualifications. You understand that your personal information will be used solely for recruitment purposes and that you can withdraw your consent at any time by contacting us at or . Message frequency may vary. Msg & data rates may apply.

 

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