Lead Machine Learning Engineer- Data - job 3 of 20

As a Lead Machine Learning Engineer - Data at Visa, your primary responsibilities encompass designing and architecting robust data engineering platforms, overseeing the entire data lifecycle—from acquisition to analysis—and developing innovative solutions like a synthetic data engine. You'll also mentor junior engineers and ensure the security and compliance of all data solutions while partnering with stakeholders to craft actionable data-driven strategies.

To excel as a Lead Machine Learning Engineer - Data at Visa, you'll need a solid background in machine learning and data engineering. A degree in Computer Science, Data Science, or related field is essential, alongside extensive experience in developing data platforms and a strong grasp of AI technologies. Familiarity with data security, governance, and compliance regulations is also crucial.

At Visa, the Lead Machine Learning Engineer - Data position offers extensive growth opportunities as the team is rapidly expanding and innovating in the AI space. By staying on top of emerging technologies and trends, you'll have the chance to influence key projects and lead high-performing teams, paving the way for advancement within Visa's technology organization.

In the role of Lead Machine Learning Engineer - Data at Visa, you will work with advanced technologies across machine learning, data pipelines, data warehouses, and synthetic data engines. You'll also explore Generative AI and Agentic AI technologies, fostering the adoption of innovative methods that drive strategic data solutions.

Collaboration is a cornerstone of the Lead Machine Learning Engineer - Data role at Visa. You will partner with business stakeholders to align data strategies with business needs, lead a team of engineers in the development of projects, and work within a diverse team that values collective creativity and input, ensuring that solutions are robust and multi-faceted.

Certainly! When answering this question, highlight your hands-on experience with various machine learning algorithms, how you've implemented them in real-world scenarios, and the results achieved. Emphasize your knowledge of both supervised and unsupervised learning and any specific algorithms you’ve developed or optimized.

Discuss your understanding of data protection regulations and your approach to integrating security measures throughout the data lifecycle. Describe specific practices you've implemented for data governance and security, such as access controls, encryption methods, and procedures for compliance audits.

Mentoring is vital for team growth. Share your strategies, such as personalized coaching, regular feedback sessions, and creating development plans that cater to individual strengths. Emphasize the importance of fostering a culture of innovation and continuous learning and how you encourage questions and collaborative problem-solving.

This is your opportunity to demonstrate your project management skills and technical expertise. Narrate the project’s objectives, your role in leading the design and implementation, the challenges you faced, and how you ensured scalability. Highlight any positive outcomes or performance improvements resulting from your efforts.

In your response, showcase your commitment to ongoing learning. Discuss resources like industry conferences, webinars, online courses, publications, or professional networks you rely on to stay informed and how you apply this knowledge practically in your work at Visa.

Reflect on contemporary challenges such as data privacy, integrating AI responsibly, or managing big data across platforms. Articulate your perspective and propose potential solutions or approaches you believe could shape the future of the data engineering landscape.

Yes, working with cross-functional teams is essential for developing comprehensive data solutions. Describe a specific instance where collaboration was key to project success, alluding to how various roles contributed to achieving shared goals and enhancing project outcomes.

Talk about your understanding of synthetic data generation, its applications, and techniques. Explain your systematic approach to designing such an engine, focusing on privacy enhancement, scalability, and compliance with regulations, and how it can be integrated into existing workflows at Visa.

Emphasize that team culture significantly impacts productivity and creativity. Describe how you've contributed to a positive team culture, supported open communication, and encouraged collaboration and innovation, aligning with Visa's values of teamwork and shared success.

Share a specific scenario where you engaged with stakeholders to understand their needs, detailing how you translated those insights into a data-driven strategy. Discuss the methods you employed to ensure alignment with business objectives and the outcomes achieved from your data solutions.