Director, Internal AI & Data Science - job 19 of 39

The Director of Internal AI & Data Science at Visa will oversee the overall design and implementation of data collection, processing, and integration for both structured and unstructured data at scale. This role requires leading a team to apply advanced data science techniques, develop large-scale systems, and communicate insights to influence business decisions.

Candidates for the Director of Internal AI & Data Science at Visa should have extensive experience in data science, technical leadership, and team management. Proficiency in programming languages like Python, R, and Spark, along with a strong understanding of AI/ML technologies is essential for this role.

Success in this role means developing effective strategies to enrich Visa's core data sources, fostering innovation within the data science team, and making data-driven business insights that lead to improved decision-making. This role will significantly impact Visa's operational capabilities and growth.

The Global AI & Data Science team at Visa works closely with various departments to integrate data science capabilities across the organization. The Director of Internal AI & Data Science will facilitate this collaboration, communicating complex technical concepts to stakeholders to ensure alignment with business goals.

Key tools for the Director of Internal AI & Data Science at Visa include advanced data science platforms such as TensorFlow, PyTorch, and scikit-learn. Staying current with these technologies and utilizing programming languages effectively will be crucial for driving innovative solutions.

When addressing this question, highlight specific projects where you've implemented AI and ML techniques, focusing on the impact those projects had on data processing or business outcomes. Use concrete examples to illustrate your expertise and ability to lead initiatives.

Answering this question involves discussing your strategic approach to prioritization. Emphasize your ability to align projects with organizational goals, the importance of stakeholder input, and how you assess the potential impact and resources needed for each project.

Discuss qualities such as vision, effective communication, and the ability to inspire and mentor a team. Provide examples of how you've exhibited these traits in past roles, particularly in complex data environments.

Highlight your commitment to continuous learning by mentioning specific resources, publications, or professional groups you engage with. Discuss how staying informed has positively impacted your previous projects.

Use the STAR (Situation, Task, Action, Result) technique to detail the challenges faced, your approach to resolving them, and the successful outcome of the project. This showcases your problem-solving abilities and technical expertise.

Talk about the key performance indicators (KPIs) you use to evaluate success, such as accuracy rates, user adoption, or ROI. Include examples from past experiences to illustrate your approach in measuring and reporting on success.

Emphasize the importance of cross-functional collaboration in data science projects. Discuss how you facilitate teamwork and communication between departments to ensure alignment and maximize project success.

Illustrate your ability to simplify and visualize data findings. Share techniques you’ve used to ensure that non-technical stakeholders grasp the significance of the data and its implications on business decisions.

Describe how you encourage creative thinking, experimentation, and continuous learning among your team members. Provide examples of initiatives you've implemented that have led to innovative outcomes.

Discuss your approach to resolving conflicts by prioritizing communication and understanding different perspectives. Provide specific examples of how you have effectively managed team dynamics and fostered a collaborative environment.