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Director, Internal AI & Data Science - job 34 of 39

With data being the fuel that drives our future - our strategies, policies, and business successes around data will define our future growth prospects. Unlocking the value available through the innovative use of data on behalf of consumers, businesses, and communities is key to our future. With our ongoing commitment to Visa’s Data Values and the responsible use of data, we at Visa have a bold vision to continue to grow and accelerate our data-related businesses and capabilities. 

Led by Visa’s Chief Data Officer, the Global Data Office coordinates high-impact, complex, company-wide projects related to data business strategy, critical investments, policy development, and marketplace execution. Working closely with Visa’s Chief Privacy Officer, the Technology organization, the wider data community, and the full set of Visa’s global lines of business, the Global Data Office is evolving Visa’s data-related work with momentum and enthusiasm. Driven by its commitment to trusted use of data, Visa is seeking enthusiastic leaders who are change-agents and passionate about the future of data around the world. 
The Global AI & Data Science team under the Global Data Office is focusing on how we can better create, enrich, and validate our own data sets using both internal data and external data sources, focusing on using AI and ML models to systematically collect and process data so that it can be used as a ground truth for many uses within Visa. 

In this role, you will lead a team of data scientists contributing to develop AI driven products and services, specifically focused on development of models and for collection and integration of external data sources into our own data, to be used to enrich our understanding of how Visa technologies are used to pay and be paid. The goal is to build a next-generation repository that can keep in line with the changing ways Visa credentials can be used. 

Responsibilities include:  
Overall design, development and delivery of approaches needed for collection, processing, and integration of external, structured and unstructured data at scale, into a flexible data model. 
•    Data sources include information about business across industry segments, across global markets, and spanning social media, public, and commercial databases.
•    Transforming the above data assets into a multitude of intelligent tags and personas using appropriate, scalable, modeling techniques.

Technical Leadership:
- Develop and execute a vision for data science capabilities related to enriching internal data sets
- Lead the design and development of large-scale data science systems
- Foster innovation and experimentation within the team

Team Management:
- Lead and manage a team of data scientists
- Provide guidance and mentorship to ensure high-quality deliverables
- Foster a culture of collaboration, innovation, and continuous learning

Data Science:
- Apply advanced data science techniques to drive business impact
- Stay up to date with industry trends and emerging technologies
- Develop and maintain expertise in multiple programming languages, AI/ML, GenAI/LLMs, and data science tools 

Collaboration:
- Work closely with cross-functional teams to integrate data science capabilities
- Communicate complex technical concepts and results to stakeholders
- Influence business decisions with data-driven insights

Technical Expertise:
- Develop and maintain expertise in multiple programming languages (e.g., Python, R, Spark)
- Stay current with industry trends and advancements in data science tools and technologies (e.g.,   TensorFlow, PyTorch, scikit-learn)

Strategy:
- Develop and execute a plan for enrichment of core data sources aligned with business goals
- Identify opportunities for data science to drive business impact
Communication:
- Communicate complex technical concepts and results to stakeholders
- Present data science capabilities and results to senior leadership and business partners

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Average salary estimate

$175000 / YEARLY (est.)
min
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$150000K
$200000K

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What You Should Know About Director, Internal AI & Data Science, Visa

Are you ready to take on a pivotal role as the Director of Internal AI & Data Science at Visa in Atlanta? This is your chance to lead an innovative team dedicated to leveraging the power of data to unlock value for businesses, consumers, and communities alike. At Visa, we believe that data is the backbone of our future, and your leadership will spearhead initiatives that align with our data values and commitment to responsible data use. In this exciting position, you’ll oversee the development of AI-driven products and services, focusing on the integration of internal and external data sources. You'll design and implement scalable models that transform vast datasets into actionable insights and enrich our understanding of payment technologies. Your technical leadership will drive a culture of experimentation and innovation, nurturing a team of talented data scientists who are just as passionate about data as you are. You’ll collaborate with cross-functional teams, ensuring that your data science strategies align seamlessly with company goals, while effectively communicating complex concepts to stakeholders. If you're enthusiastic about the future of data and ready to make a substantial impact on Visa's data-driven initiatives, we want to hear from you!

Frequently Asked Questions (FAQs) for Director, Internal AI & Data Science Role at Visa
What are the primary responsibilities of the Director, Internal AI & Data Science at Visa?

As the Director of Internal AI & Data Science at Visa, you will be responsible for the overall design and execution of data-driven strategies, focusing on the collection, processing, and integration of diverse data sources. You will lead and mentor a team of data scientists, fostering innovation and ensuring high-quality deliverables that drive business impact.

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What qualifications are required for the Director, Internal AI & Data Science position at Visa?

To qualify for the Director of Internal AI & Data Science role at Visa, candidates should have extensive experience in data science, a robust understanding of AI and machine learning techniques, and proficiency in programming languages such as Python and R. Leadership experience in managing data teams and a passion for innovation in data technology are also essential.

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How does the Director, Internal AI & Data Science at Visa leverage external data sources?

In the role of Director, Internal AI & Data Science at Visa, you will develop and execute strategies for collecting and integrating external data into Visa’s existing datasets. By transforming this data into intelligent tags and personas, you will enrich internal data models and enhance the overall understanding of payment technologies.

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What is the significance of collaboration in the Director, Internal AI & Data Science position at Visa?

Collaboration is crucial in the Director, Internal AI & Data Science role at Visa. You will work closely with cross-functional teams, integrating data science capabilities across the organization. Your ability to communicate complex data insights to stakeholders will influence business decisions and foster a culture of data-driven innovation.

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What growth opportunities exist for the Director, Internal AI & Data Science at Visa?

The Director, Internal AI & Data Science position at Visa offers numerous growth opportunities as you will be at the forefront of data innovation. You will not only enhance your technical and leadership skills but also play a critical role in shaping Visa’s future data strategies and influencing industry standards in data science.

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Common Interview Questions for Director, Internal AI & Data Science
What experience do you have leading data science teams?

When answering this question, highlight specific examples of how you've successfully led and managed data teams, focusing on your mentoring style, how you foster innovation, and examples of projects where your leadership led to successful outcomes.

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Can you describe a project where you integrated external data to enhance a data model?

Provide a detailed example of a project where integrating external data was key to its success. Discuss the types of data used, the methodologies applied, and how this integration improved insights or outcomes.

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How do you stay current with emerging data science trends and technologies?

Share the resources you use to keep your knowledge up-to-date, such as online courses, industry conferences, and academic journals. Explain how continuous learning has impacted your work and kept your team innovative.

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What programming languages and tools are you most proficient in?

Discuss your proficiency in relevant programming languages, such as Python or R, and specific tools you have used for data analysis and machine learning, illustrating your hands-on experience with real projects.

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How would you develop a strategic plan for enriching core data sources?

Explain your approach to creating a strategic plan, including assessing needs, identifying opportunities for enrichment, creating timelines, and aligning with overall business goals. Use a hypothetical or past example to support your answer.

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Describe a time when you had to communicate complex technical concepts to a non-technical audience.

Provide an example that highlights your communication skills. Discuss your approach to breaking down technical jargon into understandable terms and how you ensured stakeholder engagement and understanding.

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What is your experience with AI and machine learning models?

Detail your hands-on experience with AI and ML models, including types of models you’ve developed or implemented and the outcomes they achieved. Highlight any specific toolsets or methodologies you've utilized.

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How do you approach fostering a culture of collaboration and learning within your team?

Discuss specific strategies you have implemented to encourage collaboration and continuous learning among your team members, such as regular workshops, team-building activities, or knowledge sharing sessions.

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What challenges have you faced in data science projects, and how did you overcome them?

Talk about a specific challenge you encountered in a data science project and detail the strategies you employed to address it. Emphasize your problem-solving skills and adaptability.

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How do you measure the success of data science initiatives?

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Visa Inc. operates as a payments technology company worldwide. The company facilitates commerce through the transfer of value and information among consumers, merchants, financial institutions, businesses, strategic partners, and government entiti...

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Full-time, hybrid
DATE POSTED
April 3, 2025

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