Data Scientist (AI ML, Modeling)

At Visa, a Data Scientist is primarily responsible for conducting basic data analytics and delivering advanced machine learning models. This includes validating risk models, analyzing performance both at the aggregate and issuer levels, and supporting the preparation of model testing packages for production. You'll also be analyzing transaction data, ensuring timely project delivery, and promoting big data innovations within the organization.

To qualify for the Data Scientist position at Visa, you should have a Master's Degree in areas like Statistics, Mathematics, or Computer Science. Alternatively, a Bachelor's Degree with at least 7 years of relevant experience will also be considered. Ideally, candidates will have experience in predictive modeling and a solid background in machine learning, AI algorithms, and data analysis using tools like Python, Spark, and SQL.

A Data Scientist at Visa should be proficient in programming languages such as Python and SQL, along with familiarity in advanced techniques involving big data technologies like Spark and TensorFlow. A thorough understanding of statistical methods for data analysis is also essential, alongside experience in using these tools for extracting and aggregating data.

While previous experience in the financial sector or knowledge of the payments industry can be beneficial, it is not a strict requirement for the Data Scientist role at Visa. What truly matters is your capability to handle data challenges with creativity and skill, and your willingness to learn about the industry.

Visa offers a hybrid work environment for Data Scientists, allowing you to alternate between remote work and time in the office. Employees are generally expected to be in the office 2-3 days a week, fostering both flexibility and collaboration. The Visa culture promotes innovation and teamwork, creating a dynamic atmosphere for growth and contribution.

When discussing your experience with machine learning models, emphasize specific projects where you've implemented models to solve real-world problems. Detail the types of algorithms you used, the data you worked with, and the impact your work had on the business. Be sure to mention any innovative approaches or challenges you overcame during the process.

Address this question by explaining your strategies for data cleaning and preprocessing. Discuss techniques like imputation, data transformation, and how you assess the impact of missing data on your analysis. Highlight any tools or frameworks used, illustrating your ability to ensure the integrity of your datasets.

Describe your approach to translating complex data findings into clear visualizations and stories. Discuss tools like data visualization software or dashboards that you utilize to present your results effectively, keeping the communication focused on insights that align with business objectives, rather than technical jargon.

When answering, list the big data technologies you’ve worked with, such as Hadoop, Spark, or cloud-based platforms. Elaborate on your experience using these technologies for data processing, analysis, and building scalable solutions. Giving an example of a project can help illustrate your proficiency.

Share a detailed example of a challenging data analysis project. Explain the problem you were addressing, your approach for analyzing the data, and the results achieved. Emphasize any innovative techniques you applied or collaborations that enhanced the project's success.

Talk through your model validation process step-by-step, from selecting metrics for evaluation to conducting cross-validation. Mention techniques you employ to assess the model's performance, such as ROC curves or confusion matrices, and how you use the results to fine-tune your models.

Express your commitment to continuous learning by discussing resources you utilize, like webinars, online courses, or active participation in data science communities. Mention any recent trends you find particularly exciting and how they may influence your work at Visa.

Emphasize the importance of teamwork in achieving successful outcomes. Discuss your experience collaborating with cross-functional teams, how you ensure open communication, and share insights on combining diverse perspectives to enhance analyses and projects.

Provide a specific scenario where you identified a repetitive analysis, the steps you took to automate it (e.g., with scripts or dashboards), and the benefits achieved from this automation, such as time saved or increased accuracy in results.

Articulate your passion for the role by highlighting Visa’s innovative approach and commitment to using data to solve real-world challenges. Mention your excitement to contribute to building market-relevant fraud solutions and how your skills align with Visa's mission to uplift economies through secure payments.