Principal Machine Learning Scientist

Job Description
About the Role:
From across the globe, people are increasingly relying on digital payments and mobile technology to use their money any time, make purchases online, transfer funds across borders and access basic financial services. Risk and Identity Services (RaIS) is a technology organization at Visa that builds products and services for our clients that ensures the security and reliability of these payments. We have invested heavily in advanced authentication and fraud prevention technologies, to fight fraud, enable acceptance, and support consumers.
We are looking for a Principal ML Scientist to lead our machine learning initiatives in RaIS and drive innovation in Visa's strategic products and services. As a key member of our Applied ML research and development team, you will be responsible for designing, developing, and deploying machine learning models on cloud platforms to solve complex problems and drive innovation in our products and services.
This role represents an exciting opportunity to make key contributions to Visa's strategic vision as a world-leading data-driven company. The successful candidate must have strong academic track record and demonstrate excellent software engineering skills. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.
Essential Functions:
Research and develop machine learning algorithms and models to address business challenges and improve product performance.
Collaborate with cross-functional teams including data engineers, software developers, and product managers to understand requirements and design scalable solutions.
Implement machine learning pipelines and workflows for data preprocessing, feature engineering, model training, and evaluation.
Optimize machine learning models for performance, scalability, and reliability in cloud environments.
Deploy machine learning models to cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure. Develop monitoring and alerting systems to track model performance and detect anomalies.
Collaborate with DevOps teams to automate deployment processes and ensure smooth integration with existing systems.
Stay updated with the latest advancements in machine learning, cloud computing, and deployment technologies, and apply them to improve our practices and solutions.
Communicate technical concepts and findings to both technical and non-technical stakeholders.
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.