Director, Internal AI & Data Science - job 5 of 39

The Director, Internal AI & Data Science at Visa is responsible for designing and overseeing the development of data science capabilities that enrich internal datasets. This includes managing a team of data scientists, shaping methodologies for data integration from external sources, and fostering innovation to create AI-driven products and services. The role significantly influences data-driven business decisions and requires close collaboration with cross-functional teams.

Candidates for the Director, Internal AI & Data Science position at Visa should possess strong expertise in data science, including proficiency in programming languages such as Python and R. A deep understanding of AI/ML technologies, along with experience in managing teams and delivering complex data initiatives, is crucial. Moreover, familiarity with current industry trends and the ability to communicate complex technical concepts effectively are essential for success in this role.

The Director, Internal AI & Data Science contributes to Visa’s business strategy by leveraging data to drive insights and informed decision-making. By developing scalable data models and integrating diverse data sources, this role enhances Visa's understanding of its technology's usage and business impact, ultimately facilitating growth opportunities and strategic objectives.

Key skills for the Director, Internal AI & Data Science role at Visa include advanced data science proficiency, strong leadership capabilities, and technical expertise in data processing and machine learning. Additionally, effective communication skills for presenting complex insights to stakeholders and the ability to foster a collaborative team environment are vital for driving success in this role.

The Global Data Office at Visa plays a crucial role in shaping the strategies related to data usage across the organization. For the Director, Internal AI & Data Science, this office provides a platform for collaboration with various departments, enabling the execution of high-impact projects that drive data innovation and align with Visa’s commitment to responsible data use. This environment enhances the role’s influence in achieving Visa's data-related business goals.

Effective team leadership in data science involves clear communication of goals, fostering a culture of innovation, and providing mentorship. You should emphasize leveraging individual team members' strengths to encourage collaboration, motivate through shared successes, and instill a continuous learning mindset among the team.

Staying informed about the latest trends requires a proactive approach, such as subscribing to industry publications, attending webinars, participating in workshops, and engaging with the data science community on platforms like LinkedIn. Describe ways in which you incorporate new insights into your projects and organizational practices.

A structured integration process is key. Begin by assessing the quality and relevance of external data sources, followed by defining data models that accommodate both structured and unstructured data. Develop protocols for cleaning, processing, and tagging data to ensure accuracy and usability in decision-making contexts.

Highlight a project where you effectively used AI/ML models to drive significant business impact. Discuss the problem you aimed to solve, your project strategy, the technology stack employed, and the measurable outcomes. Emphasize teamwork and stakeholder engagement throughout the project.

Effectively measuring impacts involves establishing KPIs and metrics tied to project goals. Discuss methodologies you'd use to analyze the performance of data initiatives, including user feedback, operational efficiency gains, or revenue growth resulting from your data solutions.

Begin by simplifying complex findings into actionable insights. Use visual aids to illustrate key points and avoid jargon when possible. Your goal should be to ensure comprehension among diverse audiences, linking data outcomes directly to business objectives to emphasize their relevance.

Challenges often arise in aligning project goals among diverse team members or integrating new technologies. Share specific instances and the strategies you implemented to overcome these challenges, emphasizing team support, open communication, and flexibility in adapting to new processes.

Collaboration is fundamental to understanding varied perspectives and requirements. Discuss how you prioritize shared goals, facilitate regular check-ins, and leverage input from different teams (like IT or marketing) to refine data initiatives and ensure they address broader organizational needs.

Mention the programming languages you're proficient in, such as Python and R, and your experience with data science tools like TensorFlow and scikit-learn. Highlight how these skills enable you to implement effective models and analyses tailored to specific project goals.

Fostering innovation involves creating an environment that encourages experimentation and supports risk-taking without the fear of failure. Discuss how you promote brainstorming sessions, provide resources for professional development, and celebrate creative solutions to drive team motivation and collaboration.