Director - North America Product - job 3 of 21

The Director - North America Product at Visa is responsible for analyzing data quality trends within the US Business and Commercial issuer portfolios, developing a comprehensive data quality program, and providing subject matter expertise to external partners. This role involves maintaining strong communication with stakeholders, leading cross-functional projects, and executing a clear plan for issuer reporting and changes across the US region.

To succeed as the Director - North America Product at Visa, candidates should have a robust background in data analysis, commercial payment landscapes, and project management. Strong leadership skills, interpersonal abilities, and effective communication are also essential. Familiarity with data quality management and experience collaborating with diverse stakeholders can greatly enhance a candidate's profile.

The Director - North America Product at Visa collaborates with external clients by providing expert support during issuer data quality reviews, educating stakeholders on data quality issues, and implementing a strategic plan for improvements. Building strong relationships with issuing banks and processors is crucial for ensuring alignment and effective change execution.

A Director - North America Product at Visa can expect a fast-paced, rapidly changing, and customer-focused work environment. This role involves interacting with various stakeholders and requires the ability to operate independently while making well-reasoned decisions. A hybrid work model allows for flexibility between in-office and remote work.

Key skills for the Director - North America Product at Visa includes strong leadership and interpersonal abilities, effective organizational and communication skills, and a deep understanding of data quality issues. The ability to collaborate successfully in a matrix organizational structure is also crucial for driving cross-functional projects.

In your response, focus on specific projects where you implemented data quality programs. Discuss the methods you used to analyze trends, how you engaged with stakeholders to drive changes, and any measurable improvements achieved.

Outline a systematic approach, starting from assessing current data quality issues, creating educational materials for partners, to implementing monitoring systems. Highlight the importance of communication and collaboration with cross-functional teams.

Discuss actionable strategies such as regular updates, collaborative meetings, and using data-driven insights to foster discussions. Share examples of how you’ve built relationships with various stakeholders in past roles.

Identify specific challenges like differing priorities among teams or communication gaps. Explain the strategies you deployed to overcome them, emphasizing your flexibility, communication skills, and leadership.

Provide details on specific initiatives you led to enhance data quality, including the steps taken, stakeholders involved, and the impact of those changes, ideally with quantifiable results.

Discuss the importance of establishing clear guidelines and monitoring systems. Explain how you involve team members and external partners in auditing processes to ensure ongoing compliance and improvement.

Highlight several key performance indicators such as error rates, completeness, consistency, and timeliness. Explain how you would utilize these metrics to drive continuous improvements.

Talk about proactive approaches like staying informed through industry research, attending conferences, or engaging with thought leaders. Discuss how adaptability plays a role in shaping your strategic response.

Define your leadership approach, touching on collaboration, open communication, and empowerment of team members. Share an example of a successful cross-functional collaboration experience.

Elaborate on how statistical analysis informs decision-making and strategic direction. Mention your experience with various analytical tools and methodologies, along with the insights gained that impacted data quality.