Director, A2A Risk

1 month ago

London, United Kingdom Visa Full time
Job Description

As a Director of A2A Risk & Machine Learning Payment Products, you will partner closely with the VP Product, VP Risk, Head of Data Science, and other product leaders to shape the Risk/ML product strategy and roadmap of Visa’s A2A payments and open banking products in North America. You will build a world-class risk product for A2A payments to execute on Visa’s 2030 strategy and bring it to life. The right candidate will have a strong background in risk and fraud management as well as data science and machine learning, with demonstrated experience in building high-performing risk and fraud products for payments. 

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 account to account payment ecosystem. This role represents an exciting opportunity to make key contributions to a strategic offering for Visa. The candidate will be comfortable with ambiguity, with strong attention to detail, and excellent collaboration skills.

To be successful in this role, you need to be a self-starter, highly organized, and deeply understand the mindset of consumers and the structure of globally organized fraudsters. You will need to be able to build, lead, and mentor product managers and effectively collaborate with a team of top-tier data scientists, data engineers, and software engineers. You will also need to be able to successfully handle stakeholders on all levels across the organization, in a global environment. 

Responsibilities

  • Build, own, and manage the risk, fraud & machine learning product team for Visa’s North America Account to Account payments and open banking team

  • Drive the vision, strategy, and roadmap for Visa’s A2A payments risk and data products

  • Execute your strategy & roadmap with excellence within a global team with diverse backgrounds

  • Drive key initiatives and improvements together with the data science and engineering teams to meaningfully reduce the loss exposure of the business, maximize approval rates, and create a seamless user experience

  • Partner with other product teams, technology, sales, marketing, finance and other functions to identify and execute on key risk initiatives to limit our exposure to fraud and settlement risk

  • Understand the broad set of Visa’s fraud, risk, data science, and machine learning capabilities and products and identify opportunities for collaboration and creating unique advantages

  • Define detailed risk product requirements and be the product lead for the risk engineering team to execute on the roadmap

  • Build strong relationships with key stakeholders internally and externally to execute with excellence and align closely with Visa’s risk team

  •  Responsible for driving data science & machine learning product requirements, process design, and innovation (such as big data, artificial intelligence, machine learning, graph databases) to create a cutting-edge risk decisioning system

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.


Qualifications

Basic Qualifications

  • 10 or more years of relevant work experience with a Bachelor Degree or at least 8 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 5 years of work experience with a PhD


Preferred Qualifications

  • 10 or more years’ experience in eCommerce or payments fraud and risk management with 5 or more years’ experience leading product, engineering, risk operations, or data science teams
  • Hands-on experience building high-performing risk products from the ground up in the payments space with a proven track record of successful business impact at scale
  • In-depth knowledge of payment products, rails, and functionality including the associated risks in North America (ACH, RTP, FedNow, EFT, etc.)
  • Experience developing and implementing products and infrastructure that empower data scientists to utilize advanced machine learning techniques in risk and payments utilizing open banking data
  • Proven track record partnering with architects to design risk decisioning systems that can operate at scale, under high peak load with minimal latency
  • Experience leveraging machine learning to create products to safeguard merchants against fraud, and empower them to make their own payment decisions aligned with their risk tolerance
  • Demonstrated track record of delivering business growth and hitting OKRs
  • Strong passion for fraud and risk management, data science, machine learning, and the payments space
  • Working well within a heavily matrixed organizations, multi-national experience a plus
  • Prior experience building and managing a product team focused on fraud, risk, data science & ML in payments is a must
  • Strategic thinking, thought leadership, teamwork, and relationship-building skills
  • Outstanding communication skills at all levels
  • Experience in working with agile lifecycle and/or tracking and process management tools, e.g., JIRA
  • Ability to fully understand technical architecture, APIs and overall system design
  • Ability to thrive in a fast-paced, dynamic environment and manage multiple priorities
  • Proven ability to execute via successful external partnerships, and experience evaluating vendor offerings, conducting build-buy-partner analysis, negotiating contracts, integrating, and managing relationships


Additional Information

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.

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