Director, Internal AI & Data Science - job 28 of 39

The Director, Internal AI & Data Science at Visa is responsible for leading a team of data scientists in designing and developing data science systems that enrich internal data sets. This includes overseeing the collection, processing, and integration of large-scale structured and unstructured data. Additionally, the director will transform these data assets into intelligent insights, foster innovation within the team, and influence business decisions through data-driven insights.

Candidates for the Director, Internal AI & Data Science position at Visa should possess advanced expertise in data science techniques, AI/ML, and programming languages such as Python, R, or Spark. A strong background in data modeling and system design, along with leadership experience managing a team, is essential to guide innovative projects that generate business impact.

The Director, Internal AI & Data Science plays a pivotal role in aligning data science initiatives with Visa's overarching business goals. By leveraging data and emerging technologies, this role develops strategies to enrich core data sources, communicates complex insights to stakeholders, and directly influences critical business decisions that drive growth and operational excellence.

Success as the Director, Internal AI & Data Science at Visa requires a blend of technical and soft skills. Key skills include advanced knowledge of data science methodologies, expertise in programming languages, strong leadership and mentorship abilities, exceptional communication skills to convey complex data insights to non-technical stakeholders, and a forward-thinking approach to adopting emerging technologies.

The team culture for the Director, Internal AI & Data Science at Visa is centered around collaboration, innovation, and continuous learning. The Global Data Office values open communication and encourages its leaders to foster a positive environment where team members can experiment, share ideas, and grow professionally. This culture empowers teams to push boundaries and collaboratively create data-driven solutions.

When leading a data science team, I prioritize building a culture of innovation and collaboration. This involves open communication, providing mentorship, and encouraging team members to take ownership of their projects. I also believe in setting clear goals aligned with business objectives and regularly reviewing our progress to ensure we stay on track.

Integrating structured and unstructured data requires a comprehensive strategy. I focus on developing flexible data models that can accommodate various data types, leveraging both traditional relational databases for structured data and leveraging technologies like NoSQL databases for unstructured data. I also advocate for standardized data cleaning processes to ensure consistency.

I stay updated on data science trends by regularly attending industry conferences, participating in webinars, and engaging with professional communities online. Additionally, I follow leading data science journals and blogs to keep abreast of emerging technologies and methodologies, which informs my strategic decisions at Visa.

In a previous role, I led a project aimed at enhancing customer insights through the integration of social media data with transaction data. By employing advanced machine learning models, we developed customer personas that significantly improved our targeting strategies and resulted in a 20% increase in customer engagement rates.

I prefer using Python alongside libraries such as Pandas, NumPy, TensorFlow, and PyTorch for building data models. These tools offer flexibility and computational power necessary for large-scale data processing and machine learning tasks, allowing me to develop robust models effectively.

Mentoring junior data scientists involves hands-on training, regular feedback sessions, and encouraging them to take on challenging projects. I believe in guiding them through the problem-solving process to help develop their critical thinking and technical skills while fostering an environment where they feel comfortable asking questions.

One of the biggest challenges in data integration is ensuring compatibility between disparate data sources. I overcome this challenge by establishing clear data quality standards and incorporating thorough data validation processes. By doing so, we can ensure the data is accurate and reliable before feeding it into our models.

I measure the success of data science initiatives through defined KPIs that align with business goals. This includes evaluating project outcomes based on metrics such as efficiency improvements, cost savings, customer engagement, and overall impact on business growth. Regular reviews help us assess the effectiveness and inform future strategies.

The future of data science in business lies in its integration with AI technologies and real-time data analytics. As companies continue to recognize the value of data-driven decision-making, we will see increasingly sophisticated applications of machine learning and AI that enhance personalization and operational efficiency, driving competitive advantage across industries.