Data Scientist (Visa Predictive Models) - job 27 of 33

The main responsibilities of a Data Scientist at Visa include building and validating predictive models using advanced machine learning techniques, interpreting results for a non-technical audience, and improving modeling processes through MLOps automation. Additionally, you will conduct research on emerging modeling technologies and partner with cross-functional teams to deploy models into production.

To qualify for the Data Scientist role at Visa Predictive Models, candidates typically need a strong background in machine learning, data analysis, and programming skills. A degree in a related field such as Computer Science, Statistics, or Mathematics is commonly required, along with proficiency in tools and languages like Python or R, especially with experience in advanced modeling approaches like Deep Neural Networks.

Visa ensures the effectiveness of its predictive models by conducting thorough validation and performance assessments, managing model risks according to Visa's Model Risk Management standards, and continually refining models using the latest machine learning techniques and continuous feedback from internal and external clients.

The Data Scientist role at Visa follows a hybrid working model which allows employees to alternate between remote work and office presence. Generally, employees are expected to work in the office 2-3 days a week as determined by leadership, with an overall guideline of being in the office 50% or more of the time based on business needs.

Data Scientists at Visa utilize a variety of tools and technologies, including advanced machine learning frameworks for model building, data visualization tools, programming languages like Python and R, and emerging modeling technologies such as Deep Neural Networks and LSTMs to address fraud detection and other critical business problems.

I prefer using ensemble methods and neural networks because they often provide superior performance for complex datasets. For example, Random Forest combines the strengths of multiple decision trees, while Convolutional Neural Networks are effective for recognizing patterns in large data sets.

I typically validate predictive models through techniques like cross-validation and comparing performance metrics such as accuracy, precision, and recall. This approach helps ensure that the models generalize well to unseen data and highlights potential overfitting.

I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I might employ techniques such as imputation, or if the missing data is substantial, I may choose to exclude features or instances, ensuring the remaining dataset retains its integrity.

To automate the modeling process, I utilize MLOps practices, which include setting up pipelines for data ingestion, feature engineering, and model testing. By using tools like Docker, Jenkins, and AWS, I streamline the deployment and monitoring phases.

In a recent project, I developed a model for real-time fraud detection. I approached this by starting with exploratory data analysis, identifying key features related to fraudulent activity, and then iteratively improving the model based on performance metrics while collaborating closely with a cross-functional team for feedback.

When presenting findings to a non-technical audience, I focus on crafting a clear and concise narrative that emphasizes the business value of my findings. I visualize complex data with charts and graphs, and I use analogies or straightforward language to explain technical concepts in relatable terms.

I have hands-on experience deploying models in production environments using cloud platforms like AWS. I manage the deployment by creating scalable APIs that allow consumption of the models and ensure version control and monitoring for performance and updates.

I stay updated with the latest advancements in machine learning by participating in online courses, attending webinars, subscribing to journals and blogs in the field, and being active in community forums where professionals share knowledge and experiences.

I've worked on several projects focused on fraud detection where I developed models to identify patterns of fraud through analyzing user behavior and transaction histories, employing techniques such as anomaly detection and supervised learning to enhance accuracy.

I am excited about the opportunity to work at Visa because it offers a chance to influence a key component of the global payment ecosystem. The scale of data available and the focus on using data for positive customer outcomes align perfectly with my passions and expertise.