Parallel Score is a product development firm that develops data and user-centric solutions by leveraging designs, engineering, and innovative thinking. We are a provocative product development agency that is focused on imagining and building highly-interactive and user- driven experiences that push the limits of user design and development. We dissect where our clients are and plot a course for where they want to be using an inclusive and collaborative process integrating strategy, user experience, design, and engineering from project ideation through completion. We push the limit of what our clients think is possible. We challenge status quo. Together, we’ll come up with amazing ideas that get results.
- We are seeking an experienced Data Scientist with strong expertise in graph analytics and Neo4j to build intelligent models that uncover hidden relationships and optimize decision-making. You will design and implement analytical frameworks using graph data, AI, and statistical modeling.
Must Have Capability
- Design and execute graph-based analytical models using Neo4j, Cypher, and the Graph Data Science Library.
- Develop machine learning and predictive models leveraging graph embeddings.
- Collaborate with data engineers to prepare and transform large, complex graph datasets.
- Apply algorithms such as community detection, link prediction, and centrality analysis.
- Communicate findings through data visualization and storytelling to business stakeholders.
Required Skills & Experience
- 5+ years of experience in data science or analytics.
- Proficiency with Neo4j, Cypher, and graph-based data modeling.
- Strong programming skills in Python (pandas, scikit-learn, PyTorch/TensorFlow).
- Solid foundation in statistics, feature engineering, and ML pipelines.
- Familiarity with cloud platforms and Databricks for data preparation.
Preferred
- AWS Cloud certification
- Knowledge of graph neural networks (GNNs) or advanced graph embeddings.
- Experience deploying models into production (APIs, dashboards).
Method of Application
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