Job Description

Are you ready to make an impact in shaping the future of data governance at Block? We are seeking a highly skilled and motivated Data Scientist to join our Data Governance Foundations team. As a Data Scientist focused on the Data Governance Foundation team, you will play a pivotal role in crafting and executing robust data and machine learning tooling. Your expertise in machine learning and AI will help us create efficient and automated solutions for data classification, metadata management, and data quality across various business units under Block.

You Will:

• Collaborate closely with cross-functional teams including data engineers, data analysts, engineers and legal/compliance teams to understand data governance requirements and priorities.
• Design and implement advanced models for data classification, leveraging machine learning and Generative AI to accurately categorize and label data assets 
• Develop and refine metadata enablement frameworks that facilitate easy discovery, lineage tracking, and accessibility of data assets.
• Contribute to the creation of data quality frameworks and algorithms to identify, measure, and mitigate data quality issues across the data ecosystem, utilizing machine learning approaches.
• Enable development of automated workflows and processes that enhance the efficiency of data governance operations, including monitoring, alerting, and reporting mechanisms.
• Collaborate with stakeholders to establish governance policies, standards, and best practices, ensuring alignment with regulatory requirements and industry standards.
• Proactively identify opportunities to enhance data enablement, compliance, and privacy protection, fostering a culture of continuous improvement within the data governance domain.

Qualifications

You Have:

• Master’s or Ph.D. degree in Computer Science, Data Science, Statistics, or a related quantitative field.
• 2+ years experience in data science and analytics, 
• Proficiency in programming languages such as Python, or similar, and expertise in machine learning libraries and frameworks.
• Experience working with large and complex datasets, including data from structured and unstructured sources.
• Proven ability to design, implement, and evaluate machine learning models and algorithms.
• Strong communication skills to collaborate effectively with technical and non-technical stakeholders.