Lead Payment Risk Manager

Adobe • San Jose, CA

3d

About The Position

Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! The Opportunity Adobe is building a global payments function and recruiting a seasoned payment risk specialist to join our team dedicated to managing payment fraud and related risks. You will bring both risk strategy experience from an online merchant and deep analytical capabilities to this role. Your ability to pull and analyze data will help you find ways to improve Adobe.com risk decisioning at checkout. This will allow you to block BOT attacks and prevent fraudulent transactions. You’ll balance the sensitive ratio of false positives to false negatives, and evolve prevention strategies to drive Adobe’s business objectives. You will help define payment risk policies and build programs that prevent, detect, identify and resolve fraudulent activity by customers, potential customers, and outside agents. You will support outreach programs to the large issuing banks around the world to influence how they decision Adobe transactions. You will define A/B tests and monitor performance with a control group. This improves how you identify false positives globally using statistical methods to enable better decision-making.

Requirements

• Proficiency in data science analytical tools, such as SQL, Python, Tableau etc. along with proven experience in validation and evaluation of machine learning models.
• 5-7 years of optimizing margin-risk-experience trade-offs by applying statistical models to derive risk mitigation strategies in a large-scale data environment,
• Experience defining and managing a statistically significant global payment control group in a multi-product portfolio. This group detects False Positives and develops new strategies for an external risk assessment provider.
• Strong payments risk/fraud industry experience from the merchant’s perspective in relevant data mining domains, including anti-fraud/abuse, credit card and bank transfer abuse and financial risk control
• Ability to influence others in a highly matrix-ed organization
• Strong technical skills with a focus on data access, data queries and data driven investigations (storage, extract, transform, etc.) is required.
• Analytical strength is a must.

Responsibilities

• Mitigate fraudulent payment activity involving credit/debit cards, SEPA, PayPal, and other payment methods worldwide.
• Collaborate with Adobe’s third-party risk assessment partner and internal ML team.
• Partner closely with internal collaborator organizations across Adobe including Risk/Abuse team, Chargeback team, ML Decisioning Engineers, Product Management, Payments Partnerships and Finance.
• Establish Fraud Prevention capabilities based on predictive decisioning models that you will build and maintain for Adobe to align with industry best-practices.
• Implement improvements that raise the barriers against new threats by influencing the revision of business processes and decision-making capabilities, and through detailed auditing and validation of these processes following implementation of these changes.
• Bring a “disciplined, analytical and methodical” approach to a space that can be highly reactionary and chaotic from the ever evolving fraudulent attacks in the online space.
• Define and manage risk control measurements, implement quantitative monitoring metrics, and align internal risk teams and external risk decisioning providers on risk control numeric goals, promote results-focused, data-driven data science practices.
• Experiment experimental build & hypothesis testing control groups

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees