Manager, Responsible AI @ Reuters

About the Role

In this opportunity as Manager, you will:

• Collaborate with data scientists, AI engineers, and product managers to understand AI system requirements and functionalities.
• Develop and execute comprehensive test plans to assess the performance, accuracy, robustness, and fairness of AI models and applications.
• Design and implement automated testing frameworks for efficient and continuous AI validation.
• Analyze AI model outputs, identify potential biases, errors, or limitations, and provide actionable feedback to the development team.
• Contribute to the development and implementation of responsible AI guidelines and best practices within the organization.
• Stay abreast of the latest advancements in AI validation techniques and technologies.
• Document testing procedures and results clearly and concisely.
• Collaborate with stakeholders to address and resolve identified AI validation issues.

About You

You're a fit for the role of Manager if your background includes:

• Team Leadership and Coordination: Lead and mentor a team of Responsible AI Partners and oversee the activities of the distributed Data and Model Stewards Network. Foster a collaborative environment focused on effective model governance.
• Policy and Framework Implementation: Drive the implementation and adoption of our Model Policies, Standards, and Ethics Framework across various business units and projects.
• Stakeholder Engagement: Act as a key point of contact and trusted advisor for scientists, product groups, content groups, and business stakeholders on all matters related to Responsible AI and model governance.
• Internal Consulting and Guidance: Provide expert guidance and support to project teams on embedding model stewardship principles and best practices throughout the model lifecycle.
• Risk Oversight and Mitigation: Ensure the effective identification, assessment, and mitigation of model risks within your portfolio of projects, working closely with data scientists and engineering teams.
• Performance Monitoring and Reporting: Establish and monitor key metrics to assess the effectiveness of our model governance framework and provide regular reports to leadership.
• Process Improvement: Continuously evaluate and enhance our model governance processes, identifying opportunities for increased efficiency, robustness, and alignment with evolving regulations and ethical considerations.
• Data Analysis and Storytelling: Leverage data analysis and visualization techniques to understand our content, processes, and the impact of our AI models. Narrate compelling stories based on data insights, often to non-technical audiences.