Director - North America Product - job 8 of 21

The Director - North America Product plays a vital role in analyzing data quality trends within our US Business and Commercial issuer portfolios. You'll develop and implement a data quality program that addresses issues with Acquirers, Merchants, and Visa platforms. The position involves providing subject matter expertise to external partners, maintaining monitoring systems, and offering statistical analysis on data quality trends, making it essential for maintaining our competitive edge.

Applicants for the Director - North America Product role should possess a strong background in commercial payments, alongside excellent leadership and communication skills. A proven record of managing cross-functional teams is essential, as well as expertise in data quality and analytics. The ability to work autonomously in a fast-paced environment is also a key requirement.

This role is classified as hybrid, which means you will have the flexibility to work both remotely and in the San Francisco office. The exact expectation for in-office days will be determined by your hiring manager, providing a balance between collaboration and remote work.

Collaboration is a core function of the Director - North America Product role. You will engage with various stakeholders, including external clients and internal teams such as the Commercial Data Solutions team. Building strong relationships and communicating effectively with different levels of management will be crucial to ensure the success of cross-functional projects.

The work environment for the Director - North America Product is fast-paced and dynamic, focusing on continuous adaptation to the rapidly changing commercial payment landscape. You're encouraged to thrive in a customer-focused atmosphere where you can take initiative and make independent, informed decisions.

In your response, highlight previous roles where you handled data quality initiatives, worked with commercial payments, and managed cross-functional teams. Give specific examples of successful projects where you drove improvements, emphasizing your analytical skills and leadership experience.

Articulate your strategic process for developing a data quality program, including assessing current issues, setting specific objectives, involving stakeholders, and implementing tracking mechanisms. Discuss how you would measure success and adjust the program based on feedback.

Provide an example where effective communication and relationship-building helped you address and resolve a challenge with a stakeholder. Show how your interpersonal skills helped create a productive outcome while aligning the stakeholder with your project goals.

Discuss various statistical methods you have experience with, such as regression analysis or trend analysis, and how you have applied these to monitor data quality. Explain your approach to presenting findings to different audiences.

Emphasize your organizational skills and methods for prioritizing tasks, such as using project management tools or frameworks. Share a specific example that illustrates how you successfully juggled multiple responsibilities and met deadlines.

Reflect on the importance of strategic vision, leadership, and fostering collaboration among teams. Emphasize that effective communication and maintaining focus on company goals while supporting team members are crucial.

Outline your strategy for engaging stakeholders through regular updates, feedback sessions, and ensuring their input shapes initiatives. Mention the significance of transparency and fostering a culture of collaboration.

Discuss your leadership approach, focusing on collaboration, empowerment, and open communication. Provide examples of how you encourage team members to contribute their ideas and solutions.

Share the resources you rely on, such as industry reports, webinars, or professional networks, to stay updated on trends. Highlight your commitment to continuous learning and adapting to changes in the field.

Mention specific tools you've used for reporting and analytics, explaining how they have helped you analyze data quality trends effectively. Highlight any improvements you implemented as a result of these insights.