Lead Data Scientist

Company Description

Visa is a world leader in digital payments, facilitating more than 215 billion payments transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable and secure payments network, enabling individuals, businesses and economies to thrive.

When you join Visa, you join a culture of purpose and belonging – where your growth is priority, your identity is embraced, and the work you do matters. We believe that economies that include everyone everywhere, uplift everyone everywhere. Your work will have a direct impact on billions of people around the world – helping unlock financial access to enable the future of money movement.

Join Visa: A Network Working for Everyone.

Job Description

Visa Integrity Risk (VIR) is a Global Risk group that is tasked with the role of upholding the security and integrity of the payment ecosystem through the interdiction of illegal and fraudulent activity.  This is achieved through the deployment of risk quality control and/or compliance programs.  The programs are deployed through Visa Rules and additional client guidelines and Visa performs quality control leveraging proprietary tools, specialized third party vendors, regional risk teams, and other stakeholders.   VIR also works with internal and external stakeholders to further its charter and initiatives.

This position is ideal for an experienced Data Scientist who is passionate about collaborating with business and technology partners in solving challenging illegal and fraudulent activity. You will be a key driver in the effort to define the shared strategic vision for the Integrity Risk platform and defining tools and services that safeguard Visa’s payment systems.

The right candidate will possess strong ML and Data Science background, with demonstrated experience in building, training, implementing and optimized advanced AI models for payments, risk or fraud prevention products that created business value and delivered impact within the payments or payments risk domain or have experience building AI/ML solutions for similar industries.

A successful candidate is a technical leader with the ability to engage in high bandwidth conversations with business and technology partners and be able to think broadly about Visa’s business and drive solutions that will enhance the safety and integrity of Visa’s payment ecosystem. The candidate will help deliver innovative insights to Visa's strategic products and business. This role represents an exciting opportunity to make key contributions to strategic offering for Visa. This candidate needs to have strong academic track record and be able to demonstrate excellent data science and software engineering skills. The candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.

Essential Functions

• As a Lead data scientist in Integrity Risk team, you will help design, enhance, and build next generation fraud detection solutions in an agile development environment.
• Develop ongoing Management Information Systems (MIS) that provides oversight in Visa Integrity Risk program activity, including trends and discovery tool effectiveness
• Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with product stakeholders.
• Experiment with in-house and third-party data sets to test hypotheses on relevance and value of data to business problems.
• Build needed data transformations on structured and un-structured data.
• Build and experiment with modeling and scoring algorithms. This includes development of custom algorithms as well as use of packaged tools based on machine learning, data mining and statistical techniques.
• Devise and implement methods for adaptive learning with controls on effectiveness, methods for explaining model decisions where necessary, model validation, A/B testing of models.
• Devise and implement methods for efficiently monitoring model effectiveness and performance in production.
• Devise and implement methods for automation of all parts of the predictive pipeline to minimize labor in development and production.
• Contribute to development and adoption of shared predictive analytics infrastructure
• Mentor and train other team members on key solutions
• Able to work on multiple projects and initiatives with different/competing timelines and demands.
• Present technical solutions, capabilities, considerations, and features in business terms. Effectively communicate status, issues, and risks in a precise and timely manner
• Build-out Visa Transaction Laundering Detection models, l