Director, Internal AI & Data Science - job 25 of 39

The Director, Internal AI & Data Science at Visa has several key responsibilities including the oversight of the design, development, and delivery of data collection and processing strategies. You’ll lead the development of large-scale data science systems, manage a team of talented data scientists, and ensure the seamless integration of external data sources with internal datasets. Additionally, influencing business decisions through data-driven insights and fostering innovation within the team are crucial aspects of this role.

For the Director, Internal AI & Data Science position at Visa, candidates typically need a strong background in data science, with expertise in programming languages like Python or R. An advanced degree in a related field and extensive experience leading data science teams are also essential. Familiarity with emerging technologies such as AI/ML, GenAI/LLMs, and proficiency with tools like TensorFlow and PyTorch will be advantageous.

The Director, Internal AI & Data Science plays a crucial role in aligning data science capabilities with Visa’s business goals. By developing strategies for enriching core data sources and implementing advanced analytics, the director can generate actionable insights to guide decision-making. Your work will directly impact the effectiveness of Visa technologies and customer experiences while promoting a data-driven approach across the organization.

Successful candidates for the Director, Internal AI & Data Science role at Visa should possess strong leadership skills, technical expertise in data science technologies, and the ability to communicate effectively with various stakeholders. Skills in hands-on data analysis, proficiency in programming languages, and familiarity with large-scale data systems are also necessary for achieving success in this position.

As a hybrid position, the Director, Internal AI & Data Science at Visa allows for a mix of remote and in-office work. Specific expectations regarding in-office days will be communicated by your hiring manager. This flexibility is designed to foster a healthy work-life balance while ensuring collaboration with your team and stakeholders.

When answering this question, focus on specific examples of how you've successfully managed and mentored a data science team. Highlight your leadership style, the challenges you faced, and how you fostered an environment of innovation and collaboration.

In your response, detail a structured approach to data integration. Discuss methodologies you have successfully used in previous roles to link disparate data sources, including examples of tools and techniques that facilitated this process.

Discuss the resources you rely on to keep current with industry trends, such as attending conferences, participating in webinars, following industry leaders on social media, or reading relevant publications. This shows your commitment to continuous learning.

Be prepared to talk through your methodology for product development, including phases like research, ideation, design, and testing. Use examples from past experiences to illustrate how you've brought AI-driven products from concept to reality.

Your answer should outline specific techniques or frameworks you follow for managing data systems, including data governance, orchestration, and real-time processing. Discussing your experience with scalable architectures will be beneficial.

Highlight your strategies for fostering interdepartmental collaboration. Discuss how you facilitate communication, establish shared goals, and use collaborative tools to ensure data science initiatives align with business needs.

Talk about the programming languages you excel in, such as Python or R, and the data science tools you're familiar with, such as TensorFlow or PyTorch. Mention how your proficiency with these technologies has contributed to your previous work.

Provide a specific example that demonstrates your ability to leverage data insights for decision-making. Explain the scenario, the insights derived, and the resultant impact on the business, showcasing your analytical mindset.

Reflect on real challenges you've encountered, such as talent management or project roadblocks, and explain your approach to problem-solving. Emphasize how you turned those challenges into learning opportunities.

Share your vision for how you see AI and data science shaping the payments landscape. Discuss upcoming trends and innovations that excite you and how they could benefit a leading company like Visa.