Director, Internal AI & Data Science - job 20 of 39

The Director of Internal AI & Data Science at Visa is responsible for leading a team of data scientists in the design and development of advanced data science systems. This includes overseeing the collection, processing, and integration of external data into Visa's existing data models. The role also involves strategic leadership to influence business decisions through data-driven insights and fostering a collaborative team environment.

To qualify for the Director of Internal AI & Data Science position at Visa, candidates need to have extensive experience in data science, AI, and machine learning. Proven leadership skills are crucial, along with proficiency in programming languages such as Python and R. A deep understanding of data modeling techniques and effective communication skills to present complex technical concepts are also essential.

The Director of Internal AI & Data Science can significantly contribute to Visa's data strategy by developing and executing plans for enriching core data sources and leveraging AI-driven insights to enhance business processes. This role involves identifying new opportunities to utilize data science for broader business impacts, aligning technical capabilities with Visa’s strategic goals.

Visa promotes a collaborative and innovative work culture for the Director of Internal AI & Data Science role. The environment encourages continuous learning and experimentation. Team management is focused on mentorship and fostering a culture where new ideas thrive, allowing for impactful contributions to the broader corporate strategy.

The Director of Internal AI & Data Science at Visa will work with a variety of tools and technologies, including programming languages like Python and R, as well as data science tools such as TensorFlow, PyTorch, and scikit-learn. Staying current with industry trends and advancements in data science will be a key part of the role.

When answering this question, highlight specific examples of how you've guided your team through complex projects, mentored junior data scientists, and fostered an environment of innovation. Discuss your leadership style and how it's contributed to successful project outcomes.

Discuss your experience in designing and implementing systems that facilitate seamless data integration. Explain your methodology for evaluating data quality and the steps you take to ensure the resulting structure serves business needs effectively.

Share specific examples of AI/ML models you’ve developed, including the project goals, the challenges faced, and the impacts of the models on business outcomes. Your answer should illustrate your technical proficiency and problem-solving capabilities.

Explain your strategy for collaborating with stakeholders to understand business needs and how you translate those into data science initiatives. Providing examples of past projects that yielded significant business insights can bolster your answer.

Discuss your proficiency in different programming languages and tools relevant to data science, emphasizing any favorite frameworks or libraries that you prefer and why. Highlight any projects where these tools have led to successful outcomes.

Mention specific resources such as industry publications, conferences, online courses, and communities that you engage with to remain at the forefront of the field. Sharing how you've applied new insights or technologies in your work can show your commitment to continuous learning.

Use the STAR (Situation, Task, Action, Result) method to structure your response. Describe the context of the challenge, what was at stake, the steps you took to resolve it, and the successful result achieved.

Emphasize your ability to tailor your communication style based on your audience, including strategies you've employed to simplify complex concepts. Providing an example where your insights led to actionable decisions is also effective.

Discuss your approach to task prioritization, which often involves assessing project impact, deadlines, and resource availability. Illustrating how you balance short-term demands with long-term strategic goals can add depth to your answer.

Describe your views on collaboration, highlighting your experience working with cross-functional teams. Share specific instances where collaboration led to innovative solutions or improved project outcomes, emphasizing the importance of diverse perspectives.