Data Scientist with Neural Networks AND Machine Learning

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: 

o   Link Prediction

o   Node Classification

o   Recommendation Systems

o   Network Analysis

o   Knowledge Graph Analytics

o   Fraud Detection

o   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: 

o   Graph Convolution Networks (GCN)

o   Graph Attention Networks (GAT)

o   GraphSAGE

o   Heterogeneous Graph Networks

o   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.