Lead Machine Learning Engineer- AI Experimentation Platform - job 16 of 20

As a Lead Machine Learning Engineer at Visa, your primary responsibilities include leading the development of machine learning platforms and applications that solve business challenges, as well as creating efficient and testable code in various programming languages. You'll implement machine learning pipelines, work collaboratively with technical staff, and present technical solutions in business terms to ensure alignment with Visa’s strategic goals.

To apply for the Lead Machine Learning Engineer position at Visa, candidates should hold a degree in Computer Science, Data Science, or a related field, with significant experience in machine learning and AI technologies. Proficiency in programming languages such as Python, Java, or Scala is crucial, along with expertise in developing and managing machine learning pipelines and applications.

The AI Experimentation Platform (AIEP) at Visa drives innovation by developing an environment that encourages AI experimentation. This team leverages advanced machine learning techniques and cutting-edge technologies to create solutions that enhance product performance and operational efficiency, signaling Visa's commitment to technological advancement and maintaining a competitive edge.

Essential skills for a Lead Machine Learning Engineer at Visa include expertise in machine learning algorithms, strong programming skills in languages such as Java or Python, and the ability to effectively design and implement machine learning workflows. Additionally, excellent communication skills to convey technical solutions and collaborate with various stakeholders are vital.

A typical day for a Lead Machine Learning Engineer at Visa involves designing and developing machine learning applications, collaborating with team members to ensure project alignment, and actively engaging in implementing machine learning pipelines. You’ll also spend time problem-solving, managing project risks, and communicating progress with various stakeholders—all while contributing creatively to innovative AI solutions.

Being knowledgeable about frameworks like TensorFlow, PyTorch, or Scikit-learn is important. Discuss how you've used them in past projects and highlight specific use cases where they contributed to your success.

Share a project that showcases your skills and the impact it had. Detail the challenges faced, your approach, the technologies used, and the outcomes achieved.

Feature engineering is crucial in machine learning. Explain your systematic approach to selecting, modifying, or creating features that improve model performance, and include examples of successful feature engineering from your projects.

Discuss the various metrics and techniques you use for model evaluation, such as cross-validation, ROC curves, or confusion matrices. Emphasize your focus on ensuring that models generalize well to unseen data.

Explain methods you have used to handle missing data, such as imputation, deletion, or using algorithms that accept missing values. Provide examples to demonstrate your practical experience.

Talk about the key components: data collection, preprocessing, training, evaluation, and deployment. Highlight the importance of scalability, efficiency, and monitoring throughout the pipeline.

Discuss your experience with Git or other version control systems, illustrating how you've used them in collaborative settings to manage code effectively and ensure versioning of machine learning models.

Talk about your commitment to ethical AI practices, such as ensuring fairness, transparency, and accountability in your models. Provide examples of how you've incorporated these principles in past projects.

Emphasize your adaptability in hybrid settings, detailing how you've collaborated effectively with remote and in-office colleagues, ensuring clear communication and productivity.

Articulate your passion for AI and machine learning, and how Visa's innovative initiatives align with your professional goals. Highlight your desire to contribute meaningfully to Visa's forward-thinking approach.