The Staff ML Scientist will collaborate with a team to conduct world-class applied AI research on financial payments data, driving innovation in alignment with Visa's strategic vision by incubating new data- and AI-powered products and enhancing existing applications with machine learning and AI. 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 statistical, machine learning and software engineering skills. You will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.
Essential Functions
- Develop and apply cutting-edge algorithms and models, ranging from classical machine learning to deep learning techniques, including advanced neural network architectures such as Transformers, Graph Neural Networks (GNNs), and other emerging paradigms.
- Pioneer and apply novel data science, deep learning, and AI methodologies to address unique business challenges and drive innovation.
- Stay up-to-date with the latest research in machine learning, deep learning, and neural network architectures, integrating relevant advancements into business solutions.
- Build, experiment with, and implement statistical, machine learning, and deep learning algorithms - including custom techniques as well as industry-standard tools.
- Devise and apply advanced methods for explainability and interpretability of deep learning models, including mechanistic interpretability and model transparency techniques.
- Develop and implement adaptive learning systems, as well as methods for model validation, A/B testing, and robust performance evaluation.
- Collaborate with data engineers, software developers, product teams, and business stakeholders to translate business requirements into impactful machine learning solutions.
- Communicate complex technical concepts, findings, and recommendations clearly to both technical and non-technical audiences.
- Work with both structured and unstructured data, experimenting with in-house and third-party datasets to evaluate their relevance and value for business objectives.
- Automate all stages of the predictive pipeline to streamline development and minimize manual intervention in both development and production environments.
This is a hybrid position. Expectation of days in office will be confirmed by your Hiring Manager.