Lead Machine Learning Engineer- Data - job 8 of 20

As a Lead Machine Learning Engineer- Data at Visa, your responsibilities encompass leading the design and architecture of scalable data engineering platforms, overseeing the data lifecycle from acquisition to analysis, and developing innovative solutions like synthetic data engines. You will also mentor junior engineers and promote best practices in data security and compliance while enabling the integration of GenAI technologies within existing workflows.

To qualify for the Lead Machine Learning Engineer- Data role at Visa, candidates should have a strong background in machine learning and data engineering, along with expertise in designing data pipelines and data infrastructure. Proficiency in AI technologies and experience with statistical techniques are highly valuable. A positive attitude towards mentoring and leading teams is also essential.

In the role of Lead Machine Learning Engineer- Data at Visa, you will work on robust and scalable data engineering platforms, including cloud-based and on-prem data lakes, data warehouses, and pipelines. Your focus will be to ensure they are efficient and capable of handling the vast data that supports our innovative solutions and operational needs.

Visa prioritizes career development for the Lead Machine Learning Engineer- Data position through mentorship programs, opportunities for collaboration with industry experts, and a culture of continuous learning. You'll have access to resources to stay updated on the latest trends in data and AI, ensuring you can grow alongside the evolving demands of the industry.

The Lead Machine Learning Engineer- Data position at Visa is hybrid, requiring a mix of in-office and remote work. Specific expectations regarding days in the office will be confirmed by your hiring manager, allowing for flexibility while ensuring team collaboration and productivity.

When answering this question, discuss specific machine learning frameworks you’ve used, such as TensorFlow or PyTorch. Highlight relevant projects where you implemented these frameworks to solve real-world problems. Emphasize how your experience will benefit Visa in streamlining data processes and enhancing AI capabilities.

In formulating your response, touch on methodologies like ETL (Extract, Transform, Load) processes, scalability considerations, and the importance of data quality. Describe how you ensure that pipelines are efficient, maintainable, and secure, and any tools you utilize in designing these systems, particularly in the context of Visa.

Here, explain your knowledge of data governance practices, regulatory requirements, and security standards relevant to financial data. Discuss how you integrate security measures from the outset of data projects and how you stay compliant with internal protocols and external regulations.

Mention your approach to mentorship and team leadership, including fostering a supportive environment for learning and growth. Share examples of how you’ve previously guided less experienced teammates through projects and encourage collaboration while promoting innovation and best practices.

When answering this, discuss metrics you consider important, such as accuracy, precision, recall, and F1 score. Explain how you use cross-validation and other techniques to ensure robust evaluations, especially in the context of financial applications at Visa.

Here, you may want to mention resources you use, such as industry publications, online courses, webinars, and professional groups. Explain how staying informed enables you to bring innovative solutions to Visa that can enhance data-driven decision-making.

Use this opportunity to recount a specific experience where you tackled a difficult data-related challenge. Discuss the problem, the steps you took to analyze it, the solutions you implemented, and the positive outcomes that followed, demonstrating your expertise and problem-solving capabilities.

In your response, detail your knowledge and experience in synthetic data generation techniques. Discuss specific tools or approaches you’ve utilized and how they could be applied in real-world scenarios at Visa, particularly in situations where actual production data cannot be used.

Discuss your strategies for time and task management, emphasizing tools and methodologies that help you stay organized. Talk about how you assess project timelines, communicate with stakeholders, and balance workload while meeting deadlines, particularly in a fast-paced environment like Visa.

When discussing this, emphasize the importance of understanding current workflows, identifying potential areas for enhancement with GenAI, and ensuring staff is trained to adapt to new tools. Share how you would lead the change management process to ensure successful integration at Visa.