Senior Data Scientist (Graph & Neo4j)
Data & Analytics
WFA Digital Insight
As demand for data-driven insights surges, the role of Senior Data Scientist has become increasingly pivotal. With Proxify's global network and commitment to remote work, this position offers a unique chance for growth-oriented professionals to shine. Notably, expertise in Neo4j and graph theory is in high demand, making this an attractive opportunity for those with a strong foundation in these areas. Candidates should be prepared to showcase their technical prowess and passion for innovation.
Job Description
About the Role
Proxify is seeking a Senior Data Scientist to lead the design and implementation of graph-based models for solving high-impact problems, including fraud detection and recommendation engines. The ideal candidate will have a strong background in Neo4j and its GDS library, as well as expertise in Python, graph theory, and network science.Responsibilities
- Design and architect scalable graph schemas in Neo4j to represent complex real-world entities and their relationships
- Implement centrality, community detection, similarity, and pathfinding algorithms using the Neo4j Graph Data Science library
- Build and productionize machine learning models leveraging graph features to improve accuracy over traditional ML approaches
- Collaborate with AI engineers to integrate Knowledge Graphs into LLM workflows
Requirements
- 3+ years of hands-on experience with Neo4j and its GDS library
- Expert proficiency in Python
- Deep understanding of graph theory, network science, and linear algebra
- Experience with ETL/ELT pipelines and data preparation using SQL
How to Stand Out
- Ensure your portfolio showcases projects that demonstrate your proficiency in Neo4j and graph theory, highlighting your ability to apply these skills to real-world problems.
- Familiarize yourself with Proxify's commitment to remote work and be prepared to discuss how you thrive in a distributed team environment.
- Practice explaining complex technical concepts, such as graph-based models and machine learning algorithms, in a clear and concise manner.
- Be prepared to walk the interviewer through your process for designing and implementing scalable graph schemas, including your approach to data preparation and model optimization.
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