Director, Internal AI & Data Science - job 21 of 39

The Director, Internal AI & Data Science at Visa is responsible for leading the development and execution of data science capabilities, driving the integration of external data sources into internal data sets, and applying advanced data science techniques to create meaningful business impact. This role entails managing a team of data scientists, overseeing large-scale data science systems, and ensuring that projects align with Visa’s business goals.

To excel as the Director, Internal AI & Data Science at Visa, candidates should possess robust experience in data science, leading teams, and managing complex projects. Proficiency in programming languages such as Python and R, along with expertise in AI/ML technologies like TensorFlow and PyTorch, is essential. Candidates should also demonstrate strong leadership skills, effective communication abilities, and an understanding of emerging industry trends.

Visa's Global AI & Data Science team contributes significantly to data-driven decision-making by transforming data into insights that inform business strategies. This is achieved by enriching internal data with external sources and developing sophisticated models that enhance understanding of Visa’s technology usage. Team members collaborate across functions, ensuring that quality data science capabilities are integrated into business operations.

In the role of Director, Internal AI & Data Science at Visa, you will work with a diverse array of data sources including structured and unstructured data about businesses across various industry segments and global markets. This encompasses data from social media platforms, public databases, and commercial sources, which will be utilized to enrich Visa’s data models and develop intelligent applications.

The work environment for the Director, Internal AI & Data Science at Visa is hybrid, providing flexibility in work arrangement. Candidates can expect collaboration in office settings with a strong emphasis on team dynamics and innovation, while also having the ability to work remotely. This setup allows for a balance between personal productivity and collaborative team efforts.

When discussing your experience with AI and machine learning techniques, focus on specific projects where you developed models and integrated data sources. Highlight your familiarity with tools like TensorFlow or PyTorch and any successful outcomes from your efforts. Be ready to discuss challenges you faced and how you overcame them, emphasizing teamwork and innovative solutions.

In answering how you approach data integration, explain your methods for collecting, processing, and structuring data from external and internal datasets. Discuss your experience with ensuring data quality and the tools or frameworks you utilize to manage this process effectively, showcasing your problem-solving and analytical abilities.

Discuss your leadership philosophy and give examples of how you have fostered collaboration, innovation, and continuous improvement within your team. Share specific initiatives you've introduced to support mentorship, career development, and how these have positively impacted team performance and morale.

To ensure ethical data use, outline your commitment to responsible data handling practices that comply with privacy regulations and transparency. Provide examples of frameworks you’ve established to promote ethical considerations in data science initiatives and how you’ve encouraged your team to prioritize ethical implications in their work.

When sharing an example, aim to discuss a project that presented significant challenges and required innovative solutions. Outline your role, the team’s collaboration process, the technologies used, and the impact of the project on the business or consumers. Emphasize measurable outcomes and lessons learned.

Discuss your views on emerging trends such as automated machine learning, ethical AI, or real-time data processing. Explain how you stay updated on these trends and connect them to how they might impact Visa’s operations or strategies in the future, showcasing your forward-thinking perspective.

Outline the key performance indicators (KPIs) you believe are crucial for measuring success in data science projects. Discuss metrics related to business impact, model performance, and user feedback, providing examples of how you’ve tracked these metrics to inform adjustments and decision-making.

Explain your approach to simplifying complex data concepts for non-technical audiences. Emphasize your strategy of using visuals, analogies, or storytelling to present insights in a clear and actionable way, ensuring that stakeholders can understand and utilize the information effectively.

Discuss your commitment to continuous learning through attending conferences, online courses, and engaging with data science communities. Explain how you apply newly acquired knowledge to your work and your team’s practices, fostering a culture of learning within your organization.

When discussing cross-functional collaboration, highlight instances where you've worked with other departments, such as product development or marketing, to integrate data science solutions. Illustrate how effective communication and relationship-building skills contributed to successful outcomes, showcasing your adaptability and teamwork.