Data Scientist (Visa Predictive Models) - job 17 of 33

As a Data Scientist at Visa, you will focus on building and validating predictive models using advanced machine learning techniques. Your main responsibilities include analyzing complex data to drive business value, presenting modeling results to a non-technical audience, and conducting research with emerging technologies to solve fraud detection issues. Collaboration is key, as you'll work alongside Product Managers and Software Engineers to deploy models into production while managing model risks according to Visa's standards.

The Visa Predictive Modeling team prefers advanced tools for machine learning and modeling, including technologies like Deep Neural Networks, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models. Familiarity with MLOps practices for improving efficiency and automation during the modeling process is also highly valued. Additionally, proficiency in data manipulation and analysis tools is essential for extracting actionable insights from VisaNet data.

To excel as a Data Scientist in the Visa Predictive Modeling team, candidates typically need a strong background in data science, machine learning, or statistics. A degree in a related field, along with experience in predictive modeling and data analysis, particularly within payment or fraud detection contexts, is essential. Skills in communicating complex information clearly to diverse audiences are also important, as you'll frequently present findings to non-technical stakeholders.

Data Scientists at Visa play a crucial role in developing real-time fraud detection models that analyze transactional and identity data at merchant check-outs. By employing advanced machine learning techniques, you will create models that accurately identify and stop fraudulent activities, thus protecting both Visa’s clients and their customers. Your insights will directly impact Visa's fraud defense strategies, making you a key player in ensuring secure payment ecosystems.

A Data Scientist at Visa can look forward to a hybrid working environment, balancing time between remote and in-office work. Employees typically work from the office 2-3 days per week, fostering collaboration and communication with cross-functional teams. This dynamic setup is designed to adapt to business needs while allowing flexibility in daily operations, ultimately enhancing team productivity and engagement.

When asked about your experience with predictive modeling, be sure to highlight specific projects where you successfully implemented models. Discuss the techniques you used, the data you worked with, and the impact your models had on the business. Emphasize your familiarity with machine learning tools that are relevant to Visa’s work.

In your response, detail a systematic approach to data validation that includes checking for data quality, consistency, and completeness. Discuss any specific methodologies or tools you utilize to ensure your data is reliable, which is critical for Visa’s fraud detection efforts.

Be prepared to discuss a range of machine learning techniques, such as regression analysis, decision trees, and neural networks. Provide examples of how you have applied these methods in previous roles, ideally touching on those that are particularly relevant to Visa's focus areas.

Highlight your ability to distill complex information into engaging narratives. Share strategies you use, such as using visual aids, analogies, or simplified data summaries, to effectively communicate insights and encourage understanding among stakeholders who may not have a technical background.

Reflect on a specific instance where you identified inefficiencies in a modeling process. Describe the changes you implemented using MLOps or automation that enhanced both the speed and accuracy of your model development, showcasing your ability to drive efficiency and value.

Share your habits for continuous learning, whether through professional development courses, attending conferences, or following industry publications. Mention any specific resources or networks that keep you informed about best practices and new technologies in predictive modeling.

Talk about the collaborative nature of data science work, especially at a company like Visa. Share how you effectively communicate with cross-functional teams, such as Product Managers and Engineers, ensuring successful project outcomes while emphasizing the importance of teamwork in model deployment.

Describe a specific challenge you faced in a past project, detailing your thought process, the strategies you implemented to address the issue, and the ultimate success of your solution. This will demonstrate your problem-solving skills crucial to Visa's Fraud Detection models.

Discuss your understanding of model risk management principles and how you've applied them in previous roles. Point out your strategies to identify, assess, and mitigate risks associated with predictive modeling, aligning your response with Visa’s Model Risk Management requirements.

Be genuine in sharing your passion for data science and how it allows you to drive impactful results. Explain how roles like the Data Scientist position at Visa excite you due to their potential to influence real-world applications in payment solutions and fraud prevention.