Director, Internal AI & Data Science - job 24 of 39

The Director, Internal AI & Data Science at Visa is responsible for overseeing the design, development, and delivery of data science approaches that enrich internal data sources. This role involves managing a team of data scientists, integrating external data into Visa’s existing data framework, and fostering innovation and collaboration. You'll also be expected to communicate complex concepts to stakeholders and influence strategic business decisions with data-driven insights.

To be a successful candidate for the Director, Internal AI & Data Science role at Visa, applicants should have extensive experience in data science, particularly in leading teams and developing large-scale data systems. A strong background in programming languages such as Python and R, as well as expertise in AI/ML and data science tools like TensorFlow and PyTorch, is essential. Experience in strategy development for data enrichment aligned with business goals is also important.

As the Director of Internal AI & Data Science at Visa, you will lead a team of skilled data scientists. This team will collaborate to develop AI-driven products and services, focusing on the integration and enrichment of internal data sets with external sources. You will be responsible for providing mentorship and fostering a collaborative and innovative environment that encourages continuous learning and high-quality deliverables.

Visa places a strong emphasis on the responsible use of data as a fundamental aspect of its business strategy. As the Director, Internal AI & Data Science, you will play a key role in aligning data science initiatives with Visa’s broader business goals, focusing on driving impact through intelligent data enrichment and actionable insights. Your work will support how Visa technologies are used globally, influencing our market strategy and execution.

Success as the Director, Internal AI & Data Science at Visa requires a blend of technical expertise in programming and data science tools, along with strong leadership and communication skills. Staying current with industry trends in AI and data science is also crucial. You should be comfortable managing a team, fostering innovation, and effectively communicating complex data insights to cross-functional stakeholders.

In answering this question, focus on specific projects where you led the development of AI products, detailing the technologies used, your role in the project, and the impact it had on the business. Highlight your collaborative efforts with cross-functional teams.

Discuss the strategies and tools you’ve utilized for effective data integration, emphasizing your experience with both structured and unstructured data. Be sure to mention specific technologies like Python or Spark, and provide examples of past projects.

Share your methods for promoting a culture of innovation, such as implementing regular brainstorming sessions, encouraging experimentation, and providing opportunities for learning and development through courses or conferences.

Emphasize your conflict-resolution skills by discussing a specific instance where you successfully navigated a disagreement. Mention the importance of communication, empathy, and finding common ground.

Describe your approach to simplifying complex information. You could mention creating visuals, using storytelling methods, or summarizing key takeaways that align with business objectives to ensure clarity.

Talk about the resources you utilize such as online courses, webinars, and industry conferences, as well as your practice of networking with peers in the field to share knowledge and stay informed.

Explain your method for project prioritization, which may involve assessing business impact, resource availability, and strategic alignment. You could reference specific tools or frameworks you use to manage workflows.

Provide a detailed example, focusing on the decision-making process, the data that backed your recommendations, and the results of implementing your suggestions to demonstrate your impact on business strategy.

Discuss your understanding of a comprehensive data science strategy, highlighting elements such as clear objectives, alignment with business goals, stakeholder engagement, and continuous assessment of methodologies.

Outline the metrics you consider important for assessing project success, such as ROI, accuracy of predictions, or user adoption rates, and describe how you gather feedback to refine future projects.