Data Scientist (Visa Predictive Models) - job 28 of 33

A Data Scientist at Visa is responsible for building and validating predictive models using advanced machine learning techniques to drive business value. This involves interpreting and presenting analytical results to a non-technical audience, conducting research on new modeling technologies to address fraud detection challenges, and managing model risks per Visa's Model Risk Management standards. Collaboration with Product Managers, Data Engineers, and Software Engineers to implement model innovations is also key.

To be successful as a Data Scientist at Visa, a solid understanding of machine learning techniques is essential. Candidates typically possess a degree in computer science, mathematics, or a related field. Familiarity with tools such as R, Python, and experience with emerging modeling technologies like Deep Neural Networks is crucial. Strong problem-solving skills, the ability to communicate complex ideas, and experience in model risk management are also valued.

The Data Scientist role at Visa contributes to fraud detection by developing real-time predictive models that leverage substantial data at the merchant checkout, including transactional and identity information. These models help identify and stop fraudulent activities, enhancing the safety and security of Visa transactions and empowering clients to protect their businesses.

As a Data Scientist at Visa, you will work with advanced machine learning tools, including Deep Neural Networks, RNN, and LSTM among others. Your role will include conducting research on emerging modeling technologies to address complex fraud detection problems, improving processes through MLOps, and automation, and leveraging Visa's extensive data warehouse for insight and improvement.

Visa offers a hybrid work environment for Data Scientists, meaning you'll spend a mix of time working remotely and in the office. Employees are expected to work from the office for 2-3 set days per week, fostering collaboration and innovation. This approach allows for flexibility while ensuring the team remains connected and engaged with ongoing projects.

When asked about a complex machine learning model, focus on elaborating the problem you were addressing, the specific machine learning techniques you employed, and the impact your model had on the business outcomes. Be sure to discuss how you validated the model and any challenges you overcame to achieve success.

In response to a question about handling missing data, discuss various approaches you take such as imputation methods, using algorithms robust to missing data, or employing techniques such as data augmentation. Be clear about your thought process and how your chosen method aligns with the overall goals of the project.

When describing your approach to model deployment, emphasize your experience with MLOps practices and the importance of collaboration with cross-functional teams. Highlight steps such as testing, validation, and monitoring of the model post-deployment to ensure its effectiveness in the real world.

In discussing how to ensure unbiased models, focus on your commitment to understanding bias in data, conducting fairness evaluations, and employing techniques to mitigate bias. Explain how you continuously assess model outcomes and make adjustments as needed.

Share an example that illustrates your communication skills, highlighting how you simplified data analysis into actionable insights. Discuss the tools you used to visualize the data and how you tailored the presentation to meet the audience's needs, ensuring clarity and understanding.

When asked about fraud detection techniques, elaborate on the specific machine learning algorithms you apply and why you favor them, along with the importance of thorough data analysis and feature engineering in identifying fraudulent patterns effectively. Showcase your knowledge of contemporary approaches and methodologies.

Use this question to demonstrate your problem-solving skills. Discuss the model you created, the performance issues you encountered, the steps you took to troubleshoot and rectify the situation, and what you learned from the experience to improve future model performance.

When discussing how you stay updated in your field, mention the importance of continuous learning through attending workshops, following industry leaders, taking online courses, and participating in data science communities. Share examples of how this knowledge has influenced your work.

In response, highlight any specific challenges such as aligning goals with other departments or communication barriers. Discuss how you approached these challenges constructively and the positive outcomes that resulted from your collaborative efforts.

When answering this question, express genuine enthusiasm for Visa's mission and the opportunity to work with vast datasets in a role that combines technical skills with business impact. Mention how your background aligns with the company’s objectives and your desire to make a significant contribution to enhancing client security and experience.