“Meta Data Scientist” typically refers to a data scientist or data analyst who specializes in working with metadata. Metadata refers to data that provides information about other data. In the context of data science, metadata can include information about the structure, source, quality, and context of datasets. Here are some key aspects of a “Meta Data Scientist”:

1. Metadata Management: Meta Data Scientists focus on managing and organizing metadata associated with datasets. This includes creating metadata standards, documentation, and metadata catalogs.

2. Data Catalogs: They may work with data cataloging tools and systems to maintain a centralized repository of metadata for an organization’s datasets. Data catalogs help users discover and understand available data resources.

3. Data Quality: Meta Data Scientists assess and document the quality of data by capturing metadata related to data sources, data lineage, data transformations, and data validation processes.

4. Data Governance: They may contribute to data governance initiatives by defining data policies, standards, and data stewardship practices related to metadata.

5. Data Documentation: Meta Data Scientists create documentation that includes metadata descriptions, data dictionaries, and data lineage diagrams to aid data users in understanding and using datasets effectively.

6. Data Integration: Understanding metadata is crucial for data integration projects, as it helps in mapping data fields, data transformation rules, and data source identification.

7. Data Lineage: They may track and document the lineage of data, showing how data flows through systems and processes. Data lineage is important for compliance, auditing, and understanding the impact of changes.

8. Data Security and Privacy: Meta Data Scientists may work on metadata related to data security and privacy, including access controls, encryption, and compliance with data protection regulations.

9. Data Discovery: They assist data users in discovering relevant datasets by making metadata searchable and accessible through data cataloging tools or search engines.

10. Collaboration: Effective communication and collaboration with data engineers, data scientists, data analysts, and other stakeholders are crucial for managing metadata effectively.

11. Technical Skills: Meta Data Scientists often have technical skills in data management tools, databases, data cataloging platforms, and metadata standards like Dublin Core, Schema.org, or custom metadata schemas.

12. Domain Knowledge: Depending on the industry or domain they work in, Meta Data Scientists may need domain-specific knowledge to accurately capture and describe metadata.

13. Compliance and Regulation: They must be aware of relevant data regulations, compliance requirements, and industry standards that impact how metadata is managed and documented.