Director, Internal AI & Data Science - job 14 of 39

The Director of Internal AI & Data Science at Visa is responsible for overseeing the design and implementation of data collection and integration methods, leading a team of data scientists, and driving the development of AI-driven products and services. This includes transforming diverse data assets into intelligent tags and personas to enhance the understanding of Visa technologies.

Candidates for the Director of Internal AI & Data Science position at Visa should have extensive experience in data science and leadership, with expertise in programming languages like Python and R. A strong background in AI, machine learning, and data modeling, along with proven ability to manage teams and communicate complex concepts, is crucial.

A Director of Internal AI & Data Science at Visa should be well-versed in data science tools and technologies such as TensorFlow, PyTorch, and scikit-learn. Additionally, knowledge of scalable data modeling techniques and machine learning frameworks is essential for success in this role.

The Director of Internal AI & Data Science collaborates closely with cross-functional teams, including the Technology organization and various business lines, to integrate data science capabilities and align data strategies with business objectives. Effective communication of complex technical concepts to stakeholders is also a key aspect.

At Visa, a Director of Internal AI & Data Science has the potential for significant career advancement. Success in this role could lead to higher executive positions within the Global Data Office or related areas, especially as the importance of data and AI continues to grow within the organization.

When answering this question, highlight specific instances where you successfully managed a team, provided mentorship, and fostered a culture of innovation and collaboration. Detail your leadership style and how it contributed to the team's success in achieving data-driven goals.

Discuss your approach to project management, including setting clear objectives, implementing rigorous testing and validation processes, and encouraging continuous improvement. Mention how you utilize feedback loops and collaboration with stakeholders to enhance quality.

Explain your methodology for evaluating and selecting external data sources, the processes you implement for data cleansing and transformation, and how you ensure that these integrations align with overall business goals and data governance policies.

Share examples of advanced techniques you've utilized, such as natural language processing, deep learning, or predictive analytics. Describe the project context, the challenges faced, and the impact these techniques had on the project's outcomes.

Talk about the resources you utilize, such as industry blogs, conferences, webinars, or professional networks. Describe how you apply this knowledge in your work and encourage your team to stay informed as well.

Describe your approach to simplifying complex concepts, such as using visual aids, analogies, and focusing on business implications rather than technical jargon. It's important to gauge your audience's understanding and adjust your communication style accordingly.

Provide a detailed overview of the product, the problem it aimed to solve, your role in its development, and the measurable impact it had on the business. Emphasize collaboration, your leadership in the project, and how data drove the decision-making process.

Illustrate your problem-solving skills by providing specific examples of challenges such as data quality issues or resistance to change. Discuss the strategies you used to manage these challenges, promote stakeholder engagement, and achieve successful outcomes.

Talk about creating an environment that encourages experimentation, supporting ongoing education, and allowing team members to take ownership of their ideas. Describe specific initiatives or practices you implemented that led to innovative outcomes.

Discuss your understanding of ethical issues in data collection, analysis, and usage. Describe any frameworks or guidelines you follow, and provide examples of how you've ensured responsible use of data in projects you've led.