Director, Internal AI & Data Science - job 11 of 39

As the Director, Internal AI & Data Science at Visa, your main responsibilities include leading a team of data scientists to develop AI-driven products and services, overseeing the collection and integration of both structured and unstructured data from various external sources, and transforming data assets into intelligent tags and personas. You'll also be tasked with fostering innovation within the team and collaborating with cross-functional partners to ensure data science capabilities align with business goals.

To qualify for the Director, Internal AI & Data Science position at Visa, candidates should have extensive experience in data science, AI, and machine learning, along with expertise in programming languages such as Python and R. A strong understanding of data modeling techniques and experience in managing teams to deliver high-quality data insights are also crucial. Having a history of working collaboratively in cross-functional environments is essential to succeed in this role.

The Director, Internal AI & Data Science at Visa plays a critical role in driving business outcomes by leveraging data insights to inform strategy and decision-making. By integrating and enriching data sets through AI and machine learning, you'll be able to transform raw data into actionable intelligence that influences how Visa technologies are used across the globe, ultimately leading to enhanced customer experiences and business growth.

At Visa, as the Director, Internal AI & Data Science, you can expect a team culture that values collaboration, innovation, and continuous learning. We prioritize an environment where team members are encouraged to experiment and share knowledge while working together to achieve impactful results. Mentorship and support within the team are key components, ensuring that everyone contributes to their fullest potential while growing in their data science careers.

Candidates for the Director, Internal AI & Data Science role at Visa should be familiar with a range of technologies including data science tools and libraries like TensorFlow, PyTorch, and scikit-learn. Proficiency in programming languages such as Python and R is essential, as is an understanding of advanced AI/ML techniques and data modeling. Staying updated on industry trends will ensure you can effectively lead and innovate within our dynamic data landscape.

In your response, highlight a specific instance where you led a data science team, emphasizing the projects you managed, the outcomes achieved, and how you fostered collaboration and innovation. Emphasize how your leadership contributed to the team's success in delivering impactful data solutions.

Explain your method for evaluating and selecting external data sources, as well as the steps you take to ensure data quality and relevance. Discuss any specific strategies or tools you've used to successfully integrate these sources and share the positive outcomes of such integrations.

Discuss your preferred programming languages, such as Python or R, and highlight specific tools or libraries (e.g., TensorFlow, scikit-learn) you're proficient with. Explain how these tools have been instrumental in your previous projects to deliver results.

Share the strategies you utilize to remain updated on the latest trends, such as following key publications, participating in conferences, or engaging with professional networks. Highlight how you apply this knowledge to your work to drive innovation.

Choose a relevant example that simplifies a complex problem and showcases your analytical skills. Outline how you approached the problem, the data-driven strategies you implemented, and the results that led to improved business outcomes.

Discuss your belief in the importance of interdisciplinary collaboration and how you foster effective communication between data teams and other business units. Share specific experiences or techniques that have been particularly successful in building collaborative relationships.

Explain your approach to project prioritization based on business needs, potential impact, and available resources. Include any frameworks or methods you use to evaluate and select which projects to pursue.

Share a specific scenario where you encountered a challenge, detailing how you identified the root cause, developed a solution, and ensured the project continued progressing. Emphasize your resilience and problem-solving skills.

Discuss your commitment to mentorship and how you actively support junior team members in their professional growth. Provide examples of how mentorship positively impacted individuals and the team as a whole.

Share your insights into emerging trends such as ethical AI, advancements in machine learning, or the role of data privacy. Discuss how these trends could shape the future of data science practices and how you plan to adapt to them.