Graph Data Sceintist

Hi Professionals,

Graph Data Scientist

Remote/Canada

3 Months Contract

Experience: 5+ Years

Key Responsibilities:

• Develop and apply graph embeddings to model and analyze relationships in graph databases (e.g., Neo4j, TigerGraph) to extract meaningful patterns from connected data.
• Use graph-based machine learning techniques and knowledge graphs to solve real-world problems.
• Design, develop, and implement machine learning models and algorithms, with a focus on deep learning architectures (e.g., CNNs, RNNs, transformers).
• Collaborate with data engineers to design and maintain scalable data pipelines, leveraging technologies such as Apache Spark, Databricks, and distributed computing frameworks..
• Stay up-to-date with the latest research and innovations in NLP, computer vision, and other AI domains, and apply them to solve business challenges.
• Work with cloud platforms such as AWS, Azure, or Google Cloud Platform for model deployment, scaling, and management.

Required Qualifications:

• Bachelor's or Master s degree in Computer Science, Data Science, Applied Mathematics, or a related field. A Ph.D. is a plus.
• Proven experience (2+ years) working in data science, with a strong focus on deep learning and graph-based data science.
• Hands-on experience with graph databases (e.g., Neo4j, ArangoDB, Amazon Neptune) and knowledge of graph algorithms (PageRank, shortest path, centrality, etc.).
• Proficiency in building and tuning deep learning models using TensorFlow, PyTorch, or similar frameworks.
• Strong programming skills in Python (with libraries such as scikit-learn, pandas, NumPy) and SQL.
• Experience with embedding techniques (word2vec, node2vec, GloVe, BERT, etc.) and integrating them into graph-based models.
• Experience with cloud platforms (AWS, Azure, or Google Cloud Platform) and tools such as Kubernetes, Docker, for containerization and model deployment.
• Familiarity with Big Data technologies like Apache Spark and data querying using SQL, Hive, or Presto.

Preferred Qualifications:

• Experience with NLP techniques (transformer-based models, text embeddings).
• Understanding of MLOps practices for the end-to-end lifecycle of machine learning models (from model development to deployment).

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