Lead Machine Learning Engineer- Data - job 16 of 20

As a Lead Machine Learning Engineer- Data at Visa, you will be responsible for designing and architecting scalable data engineering platforms and infrastructures. This includes overseeing the data lifecycle, from ingestion to transformation and analysis, as well as developing a synthetic data engine to ensure compliance and data privacy. You will also champion the adoption of AI technologies and lead a team of high-performing engineers, all while collaborating with business stakeholders to provide actionable data-driven solutions.

To qualify for the Lead Machine Learning Engineer- Data role at Visa, candidates should have a strong background in machine learning and data engineering, ideally with prior experience in developing large-scale data solutions. Knowledge of data pipelines, data warehouses, and compliance regulations is crucial, along with experience in mentoring and leading teams. Familiarity with GenAI technologies is a significant plus.

Visa is committed to the career advancement of its employees, especially for positions like Lead Machine Learning Engineers. By providing access to resources, mentorship programs, and opportunities for collaboration with top industry experts, Visa fosters an environment conducive to continuous learning and innovation. Your contributions to pioneering data and AI projects will be both recognized and rewarded.

The work culture for a Lead Machine Learning Engineer- Data at Visa is dynamic and collaborative. Our team values diverse experiences and encourages creative problem-solving. You'll work on exciting challenges within a supportive environment that promotes teamwork, innovation, and having fun while delivering high-quality solutions. It's an atmosphere where ideas are valued and shared, making every day uniquely engaging.

The hybrid work model for the Lead Machine Learning Engineer- Data position at Visa allows for a flexible balance of in-office and remote work. Specific expectations for in-office days will be confirmed by your hiring manager, ensuring that you have the opportunity to collaborate effectively with your team while also benefiting from the flexibility of working remotely.

When answering this question, share specific examples of data pipelines you've built or maintained, discussing the tools and technologies you used, as well as the challenges faced and how you overcame them. Highlight the impact your work had on the overall data operations.

In your response, emphasize your understanding of data governance principles, regulatory requirements, and best practices for maintaining data security. Provide examples of policies or procedures you've implemented in the past to ensure compliance and security in data solutions.

Mention a specific project where you applied machine learning techniques successfully. Discuss the problem, your approach to developing a solution, the algorithms used, and the results achieved. Explain how this experience has prepared you for the Lead Machine Learning Engineer- Data role.

Discuss your mentoring approach, which could include hands-on guidance, regular check-ins, encouraging collaboration on projects, and sharing resources for continued learning. Highlight how mentoring contributes to team growth and innovation.

Share how you actively keep abreast of trends in the data and AI field. Mention sources like industry publications, online courses, conferences, or community events you participate in. Illustrate your commitment to continuous learning.

Provide a detailed account of a specific challenge, outlining the context, your analysis, the steps you took to resolve the issue, and the outcome. Emphasize problem-solving skills and resilience in the face of obstacles, which are crucial for the Lead Machine Learning Engineer- Data role.

Articulate your strategies for prioritizing tasks, such as using project management tools, assessing deadlines, and communicating with team members. Describe how you balance multiple projects while ensuring high-quality results.

Explain the importance of collaboration in data engineering projects. Talk about how working alongside cross-functional teams leads to more effective solutions and aligns data initiatives with business objectives. Share examples of successful collaborative projects.

In your response, discuss the importance of understanding current workflows and identifying areas where GenAI can add value. Explain your approach to pilot integrations, experimentation, and monitoring to ensure a smooth transition and measurable success.

Detail specific custom algorithms you've implemented, the challenges they addressed, and the resultant performance improvements. This is an opportunity to showcase your technical prowess and creativity in solving unique data problems.