Data Owner for AIML

As a Data Owner on the AI/ML for Ops Data Team you will play a key role in enabling the business to drive faster innovation through the use of its data while ensuring that data is of good quality and well-protected. Youll be accountable for data in the product that is created provisioned or consumed to support business objectives advanced analytics business operations and reporting. As a member of the data owner team youll collaborate with the Product Owner Analytics Design and Technology Leads to ensure that the product delivers data in a manner consistent with the quality and safety requirements of the business.

This role offers significant opportunities for learning growth and career advancement within a collaborative and innovative team.

Job responsibilities

• Data needs and consumption
  • Work with product tech and analytics partners to drive an understanding of data needs and alignment to features in product / analytics / AI roadmap
  • Identify upstream data sources detailed requirements and dependencies. Manage resolution of data needs and data dependencies so that product features / analytics / AI are enabled in a timely manner.

• Data publishing
  • Support data users by identifying data required to drive business value and helping publish this data to central platforms.
  • Influence resources to resolve identified data issues in a timely manner.

• Data Governance
  • Lead efforts on data use classifying the data appropriately documenting use cases for the data in detail aligning stakeholders on data use and presenting to the use case councils for approval

  • Assess and improve data quality for priority domains.

    Develop processes and procedures to identify monitor and mitigate data risks including protection retention destruction and storage.

• Data product mindset researching user needs and bringing as a product mindset to data including AI / ML ready enhancements.

Required qualifications capabilities and skills

• Seven years of industry experience in a data-related field.

• Domain knowledge in Data Ownership
  • Experience in data management and governance including data quality classification and use.
  • Driving data consumption from various upstream sources per product needs
  • Enhancing the machine understandability of data through modernization efforts
  • Proficiency with analyzing / wrangling data from different sources to drive insights. Leveraging these insights to drive data publishing and data products.