Data Scientist (Visa Predictive Models) - job 23 of 33

As a Data Scientist at Visa, your primary responsibilities include building and validating predictive models using advanced machine learning techniques, conducting research to address fraud detection challenges, and presenting findings to both technical and non-technical audiences. You will also partner with various teams to deploy these models while managing risks in line with Visa's Model Risk Management requirements.

To apply for the Data Scientist position at Visa, candidates typically need a strong background in data science, statistics, or a related field, with relevant experience in machine learning and predictive modeling. Familiarity with tools such as Python or R, and knowledge of technologies like Deep Neural Networks and MLOps, will significantly enhance your application.

In your role as a Data Scientist at Visa, you will work with a variety of technologies and tools, focusing primarily on advanced machine learning and predictive modeling frameworks. This includes using techniques such as RNN and LSTM, along with collaboration tools for MLOps and automation that enhance model deployment efficiency and reliability.

The Data Scientist position at Visa is a hybrid role, allowing you to work remotely while also requiring you to be in the office 2-3 days a week. This setup promotes collaboration and engagement with your team while granting you the flexibility to work from home.

The work of a Data Scientist at Visa directly contributes to the company's business by developing predictive models that drive fraud detection and identity verification. By leveraging vast data sources, these models help Visa clients mitigate risks and enhance the customer experience, ultimately fueling business growth and generating revenue for Visa.

When answering this question, highlight specific predictive modeling projects you've worked on, mention the techniques used, such as logistic regression or neural networks, and emphasize how your models drove business results or solved specific problems.

Detail your process for data cleaning and preprocessing, including how you handle missing values, categorical variables, and data normalization. Provide examples of how these steps have led to improved model performance in your past projects.

Discuss your familiarity with MLOps principles, including model versioning, testing, and continuous integration/continuous deployment (CI/CD). Provide insights on any projects where you've successfully deployed models into production environments.

Explain your understanding of model bias, its potential effects on outcomes, and your strategies for detecting and mitigating bias. Share examples where you have evaluated model fairness and adjusted your approach accordingly.

Choose an algorithm you understand well and explain its workings in straightforward terms. Discuss applications where you've successfully implemented this algorithm and any modifications you made to optimize its performance.

Highlight your communication skills by discussing specific strategies you use, such as visualizations, storytelling, and using relatable analogies. Describe a situation where your presentation led to informed decision-making.

Outline the metrics you use to evaluate model performance like accuracy, precision, recall, F1 score, or ROC AUC, and how you interpret these metrics to improve models. Providing examples can strengthen your response.

Describe specific resources you use to stay informed about the latest trends in data science, such as academic journals, online courses, or data science communities. Mention any recent trends you find particularly impactful and how you might incorporate them into your work.

Select a challenging project and outline the problem, your approach to solving it, the solution you implemented, and the results you achieved. Reflect on what you learned from the experience and how it has influenced your work.

Show your enthusiasm for Visa’s mission by discussing how your values align with theirs, the innovative work being done in predictive modeling, and your desire to contribute positively to the payment ecosystem. Personal anecdotes can help make your passion genuine.