Data Science Specialist - Python

• Ontology: Understanding of ontology principles and standards (e.g., OWL, RDF). • Data Visualization: Ability to visualize knowledge graph data for analysis and reporting. • Machine Learning: Knowledge of machine learning concepts and algorithms, especially graph neural networks (GNNs).

Digital : Scala, Digital : Python for Data Science

8-10

• Data Modeling and Design: Developing and implementing data models for knowledge graphs, defining entities, relationships, and properties. • Graph Database Expertise: Working with graph databases (e.g., Amazon Neptune, Neo4j) to store and query data. • Data Ingestion and ETL: Developing pipelines to extract, transform, and load data into the knowledge graph. • Graph Querying and Analytics: Writing and optimizing graph queries to retrieve and analyze data. • Performance Tuning: Ensuring the knowledge graph is scalable and performs well under load. • Collaboration: Working with other teams (e.g., data scientists, product managers) to understand requirements and translate them into technical specifications.

• Programming Languages: Proficiency in languages like Python, Java, or Scala. • Graph Databases: Experience with graph databases such as Amazon Neptune or Neo4j. • Data Modeling: Strong understanding of data modeling principles and ontology. • Graph Querying: Expertise in graph query languages (e.g., Cypher, Gremlin). • ETL Tools: Familiarity with ETL tools for data transformation and loading. • Software Engineering Practices: Understanding of software development lifecycles and best practices. • Problem-Solving: Ability to identify and resolve technical issues related to the knowledge graph.