Sr. ML Scientist

Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in 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 while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.

Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.

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. 

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.  

Essential Functions 

• As a Senior ML Scientist 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 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 ML scientists on the team on key solutions 

This is a hybrid position. Expectation of days in office will be confirmed by your Hiring Manager.

Basic Qualifications:
2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience

Preferred Qualifications
3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)

Training and/or deep experience in mathematical concepts underlying statistics, machine-learning, algorithms, and data structures.
Intermediate to Expert programming skills in at least one of Python, C, Scala.
Experience working with large datasets using tools like PySpark.
Experience in Java is a plus.
Experience in building and deploying models as micro-services.
Publications or presentation in recognized Machine Learning and Data Mining journals/conferences is a plus.

Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.