Machine Learning Engineer - Sr. Consultant level

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

Payments are a very exciting and fast-developing area with a lot of new and innovative ideas coming to market. With strong demand for new solutions in this space, it promises to be an exciting area of innovation. VISA is a strong leader in the payment industry and is rapidly transitioning into a technology company with significant investments in this area. 

If you want to be in the exciting payment space, learn fast and make big impacts, Ecosystem & Operational Risk technology which is part of Visa’s Value-Added Services business unit is an ideal place for you! 

In Ecosystem & Operational Risk group, Payment Fraud Disruption team is responsible for building critical risk and fraud detection and prevention applications and services at Visa. This includes idea generation, architecture, design, development, and testing of products, applications, and services that provide Visa clients with solutions to detect, prevent, and mitigate fraud for Visa and Visa client payment systems. 

This position is ideal for an experienced ML scientist who is passionate about collaborating with business and technology partners in solving challenging fraud prevention problems. You will be a key driver in the effort to define the shared strategic vision for the Payment Fraud Disruption platform and defining tools and services that safeguard Visa’s payment systems.  

The candidate for this role need to have 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.  

To be successful in this role, the candidate need to be 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 software engineering skills. The candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills. 

The ideal candidate will bring the excitement and passion to leverage Generate AI to advance existing fraud detection mechanisms and to innovate and solve new fraud use cases. This engineer will use code generation capabilities like GitHub copilot to drive efficiencies in software development. 

Essential Functions 

• As a ML Scientist - Sr. Consultant you will help design, enhance, and build next generation fraud detection solutions in an agile development environment. 

• Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with product stakeholders.  

• Work with software engineers to ensure feasibility of solutions. Deliver prototypes and production code based on need. 

• 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