at Visa
Full TimeVisa 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.
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 and Ecosystem Security & Integrity team is responsible for building critical risk and fraud detection, prevention applications and Monitoring Applications 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 Engineer 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/Ecosystem Security & Integrity Platform and defining tools and services that safeguard Visa’s payment systems.
The candidate for this role needs to have strong ML and Data Science background, with demonstrated experience in building, training, implementing and optimizing 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 needs 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.
Essential Functions
Collaborate with project team members (Product Managers, Architects, Analysts, Software Engineers, Project Managers, etc.) to ensure development and implementation of new data driven business solutions.
Drive development effort End-to-End for on-time delivery of high-quality solutions that conform to requirements, conform to the architectural vision, and comply with all applicable standards. Responsibilities span all phases of solution development.
Collaborate with senior technical staff and PM to identify, document, plan contingency, track and manage risks and issues until all are resolved
Present technical solutions, capabilities, considerations, and features in business terms. Effectively communicate status, issues, and risks in a precise and timely manner.
Coaching and mentoring junior team members and evolving team talent pipeline.
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.
Basic Qualifications:
• 8 or more years of relevant work experience with a Bachelor Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD
Preferred Qualifications:
• 9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD in Computer
Science or related field
• Expert in leading-edge areas such as Machine Learning, Deep Learning,
Stream Computing and MLOps
• High level of competence in Python, Perl, Java, Scala, and/or Unix/Linux
scripts highly preferred
• Extensive experience with SAS/SQL/Hive for extracting and aggregating data
• Experience with Big Data and analytics leveraging technologies like Hadoop,
Spark, Scala, and MapReduce
• Deep learning experience working with TensorFlow and Natural Language
• Processing experience are highly preferred
• Experience with one or more common statistical tools such SAS, R, KNIME,
Matlab
• Experience in developing large scale, enterprise class distributed systems of
high availability, low latency, & strong data consistency
• Experience in architecting solutions with Continuous Integration and
Continuous Delivery in mind
• Familiarity with in distributed in-memory computing technologies
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.
