ML Scientist

The ideal candidate will bring the excitement and passion to leverage traditional and generative AI to advance existing fraud detection mechanisms and to innovate and solve new fraud use cases. This engineer will help design, enhance, and build next generation fraud detection solutions in an agile development environment. 

Essential Functions 

• Develop new models and re-train existing ones, evaluate performance, and optimize scores. Preferable to have data science knowledge and experience in designing, developing, and implementing Deep Learning methodologies and scalable ML models.

• Devise deep learning architectures and algorithms for graph-based data, integrating Graph Neural Networks (GNNs) and advanced graph representation learning techniques.

• Implement efficient methods for monitoring model effectiveness and performance in production.

• Build ETL pipelines using Spark, Python, HIVE, Scala, or Airflow to process transaction and account-level data, and standardize data fields from various sources.

• Experiment with and develop custom algorithms for modeling and scoring, utilizing machine learning, data mining, and statistical techniques.

• Collaborate with Data Scientists, Data Engineers, Software Engineers, and cross-functional partners to design and deploy AI/ML solutions and products.

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.

Visa is not offering relocation assistance for this role.