Director, Internal AI & Data Science - job 9 of 39

To be considered for the Director of Internal AI & Data Science position at Visa, candidates should possess advanced knowledge in data science, particularly in AI and ML applications. Proficiency in programming languages, such as Python and R, is crucial. A strong background in managing data science teams, along with experience in developing scalable data solutions is essential. Candidates should also have excellent communication skills to convey complex concepts to leadership and stakeholders, alongside a keen understanding of data-related industry trends.

As the Director of Internal AI & Data Science at Visa, you will oversee the design and implementation of strategies for collecting and integrating both structured and unstructured external data at scale. You will lead a talented team of data scientists, fostering an innovative culture while ensuring high-quality deliverables. Responsibilities also include applying advanced data science techniques to drive business outcomes and presenting insights to senior leadership to inform data-driven decisions.

Success as the Director of Internal AI & Data Science at Visa requires a blend of technical and leadership skills. You’ll need expertise in multiple programming languages and data science tools, coupled with a strong foundation in AI and ML methods. Additionally, strong team management abilities, innovative thinking, and effective communication skills are vital for leading your team and presenting insights to stakeholders.

The Director of Internal AI & Data Science plays a pivotal role in shaping Visa’s data strategy by developing and executing a vision for enriching core data sources. This position directly impacts business decisions by translating complex data insights into actionable strategies that align with company goals, ultimately enhancing the company’s ability to innovate and provide data-driven solutions.

Visa is a fantastic workplace for the Director of Internal AI & Data Science due to its commitment to innovation and responsible data use. With access to cutting-edge technologies, a collaborative environment, and a strong focus on personal development and continuous learning, Visa empowers its employees to take leadership initiatives that influence the future of payment technologies. Plus, the hybrid work model strikes a great balance between flexibility and connection with your team.

It's important to highlight specific projects where you used AI and ML models, detailing the problem you solved, the approach you took, and the results achieved. Emphasize your role in the project, the technologies used, and how these models drove actionable insights for business decisions.

Discuss your management style, emphasizing collaboration and open communication. Provide examples of how you fostered a culture of innovation and continuous learning, alongside specific methodologies you've used to inspire your team and ensure high-quality outputs.

Detail your approach to data integration, including the tools and techniques utilized to collect, process, and analyze both structured and unstructured data. Mention any frameworks or methodologies you follow to ensure a systematic and scalable process.

Share an instance where your communication skills played a key role. Highlight how you translated technical jargon into layman's terms, ensuring stakeholders understood the significance of the data and its impact on business outcomes.

Discuss the trends that excite you, such as advancements in GenAI/LLMs or the increased focus on ethical AI. Explain how you plan to leverage these trends at Visa to enhance data-driven initiatives and drive business success.

Talk about your approach to prioritization and time management, emphasizing your ability to assess project impact versus urgency. Provide examples where you facilitated discussions to reach consensus, ensuring the team remains focused on key objectives.

Provide an overview of the data modeling techniques you’ve used, including examples of projects where you successfully applied them. Discuss how these techniques contributed to better understanding data and informing business strategies.

Explain your understanding of data privacy regulations and your strategies for ensuring compliance, such as collaborating with legal teams, implementing privacy-by-design frameworks, and regularly training your team on data governance best practices.

Share the resources, communities, and platforms you engage with to stay on the pulse of the data science industry. Highlight any conferences, webinars, or professional groups that you participate in to deepen your knowledge.

Discuss specific initiatives or practices you’ve implemented to encourage experimentation and creative problem-solving within your team. Mention how you recognize and reward innovative thinking to maintain high engagement levels.