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.
Ecosystem Risk Programs 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. ERP also works with internal and external stakeholders to further its charter and initiatives.
This position is ideal for an experienced Data Analyst 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 Ecosystem Risk Programs platform and defining tools and services that safeguard Visa’s payment systems.
The right candidate will have a proficiency in conducting in-depth analyses and deep-dives on large volume of data and eventually transform these analyses into dashboards/reports. the 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 SME who can 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
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:
• 3 years of work experience with a Bachelors Degree or an Advanced Degree (e.g. Masters, MBA, JD, MD, or PhD)
• Bachelors or Masters in Computer Science, Operations Research, Statistics, or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis
• Relevant exposure to modeling techniques such as logistic regression, Naïve Bayes, SVM, decision trees, or neural networks
• Proficiency in leading-edge areas such as Machine Learning, Deep Learning, Stream Computing and MLOps
• Experience with Python, SQL, PySpark and Hive on data and analytics solutions
• Experience with data visualization and business intelligence tools like Tableau or Power BI
• Excellent analytic and problem-solving capability combined with ambition to solve real-world problems
• Excellent interpersonal, facilitation, and effective communication skills (both written and verbal) and the ability to present complex ideas in a clear, concise way
• Have great work ethics, and be a team player striving to bring the best results as a team
• Ability to work with internal product development and engineering teams to deliver products on schedule and with great quality. Comfortable in a heavily matrixed organization
• Strong analytical and problem-solving abilities, ability to use hard data and metrics to back up assumptions and evaluate outcomes
• Ability to juggle multiple priorities and make things happen in a fast-paced, dynamic environment
• Ability to understand both business and technical concepts
Preferred Qualifications:
• High level of competence in Python, Spark, and Unix/Linux scripts
• Real world experience using Hadoop and the related query engines (Hive / Impala)
• Experience with Natural Language Processing and Deep Learning algorithms is a plus
• Modeling experience in card industry or financial service company using for fraud, credit risk, payments is plus
• Experience with data visualization and business intelligence tools like Tableau or Power BI
• Own delivery of multiple projects from technical requirements and quality assurance perspective.
• Self-starter who can communicate with a deep understanding of the company needs and enable people to move forward through complexity
• Demonstrated cross-functional competence, having led/coordinated teams across functional areas in large, matrixed organizations
• Flexible and creative thinker with strong execution skills, generate out-of-the-box solutions, manage ambiguity, anticipate the impact of decisions/initiatives and able to move seamlessly from high level concepts to details
• Strong hold on MS Excel and PowerPoint
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.