Director, Internal AI & Data Science - job 27 of 39

As a Director of Internal AI & Data Science at Visa, your primary responsibilities will include leading a team of data scientists to develop AI-driven products and services. You will oversee the design, development, and delivery processes for collecting and integrating both structured and unstructured data at scale into flexible data models. Additionally, you will collaborate with cross-functional teams, communicate complex technical concepts to stakeholders, and develop strategies for enriching core data sources aligned with Visa's business goals.

To be successful as the Director of Internal AI & Data Science at Visa, candidates should possess a strong background in data science with expertise in multiple programming languages like Python, R, and Spark. A deep understanding of AI/ML, generative AI, and data science tools such as TensorFlow and PyTorch is essential. Additionally, proven experience in team management, innovation, and collaboration in cross-functional settings is highly valued.

The Director of Internal AI & Data Science plays a vital role in shaping Visa's business strategy by driving data science initiatives that enhance internal data sets. This position involves identifying and implementing innovative approaches to leverage data for better decision-making, developing predictive models that inform business strategies, and presenting insights to senior leadership to influence key business outcomes.

In the role of Director of Internal AI & Data Science at Visa, you will lead a dynamic team of data scientists. Your leadership will be crucial in providing guidance, mentorship, and support to foster a culture of collaboration, innovation, and continuous learning. You will encourage your team to experiment with new ideas, maintain high standards for deliverables, and stay updated on industry trends.

The Director of Internal AI & Data Science position at Visa is hybrid, allowing for a flexible work environment that combines both in-office and remote work. The expectation for in-office days will be determined by your hiring manager, promoting a balance between collaboration and personal work preferences.

When answering this question, highlight specific projects where you implemented AI or machine learning techniques. Discuss the tools and frameworks you used, the problems you aimed to solve, and the outcomes achieved. This shows your practical understanding of AI/ML technologies and their application in data science.

For this question, discuss your management style, your focus on mentorship, collaboration, and fostering an innovative environment. Share examples of how you've previously led teams, managed projects, and ensured high-quality outputs from team members. This showcases your leadership skills and understanding of team dynamics.

Provide a clear example that includes the context, the data analytics performed, and the resulting business decision. Emphasize how the insights derived from data led to tangible benefits for the company, demonstrating your ability to drive business impact through data.

Discuss specific methods you've used such as data wrangling, ETL processes, and the use of data lakes or other repository technologies. Highlight your understanding of the challenges with different data types and your solutions for effective integration.

Talk about your engagement with the data science community through conferences, journals, online courses, or resources like blogs and research papers. This shows your commitment to professional growth and staying current in a rapidly evolving field.

Outline a broad strategy that includes assessing the current data landscape, identifying gaps, and leveraging advanced tools and techniques for data enrichment. Discuss how this aligns with Visa's overall business goals, showcasing your strategic thinking.

Share your approach to simplifying technical jargon into relatable terms and using visual aids where appropriate. Mention experiences where you successfully communicated insights to stakeholders, emphasizing the importance of clarity in data-driven discussions.

Reflect on specific challenges, whether they were technical hurdles, resource constraints, or team dynamics. Explain the strategies you employed to overcome these obstacles and how they led to project success, illustrating your problem-solving capabilities.

Be honest about a specific challenge you faced in a project, what went wrong, and the lessons learned. Focus on your ability to adapt, learn from mistakes, and implement changes for future success, which demonstrates resilience and growth mindset.

Articulate a mix of technical skills, such as proficiency in AI/ML and programming languages, along with soft skills like leadership, communication, and collaboration. Emphasizing a balance of technical and interpersonal skills highlights your well-rounded suitability for the role.