Job Title: Data Scientist with Neural Networks AND Machine Learning
Location: Remote
Duration : Long-term contract
Salary range: $50-55/Hr (W2)
Note: No visa sponsorship
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) 2. Demonstrated Graph ML Delivery Experience
- 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.
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. 4. GNN Frameworks
- Python & Advanced Machine Learning Strong experience with:
- Python
- NumPy
- Pandas
- Scikit-learn
- Data processing and feature engineering
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.
About ApTask:
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 comprehensive suite of services, including staffing and recruitment solutions, managed services, IT consulting, and project management. With a focus on excellence, collaboration, and innovation, ApTask provides unparalleled opportunities for professional growth and development. As a member of the ApTask team, you will have the chance to connect businesses with top-tier professionals, optimize workforce performance, and drive success across diverse industries. Join us at ApTask and be part of our mission to empower organizations to thrive while fostering a diverse and inclusive work environment.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
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