Job description
About the Team: At Sift, the Product Management team owns the vision and oversees the execution of all of Sift’s products, from ideation to product launch. We are dedicated to safeguarding our customers’ digital platforms by preventing unauthorized account access and detecting malicious, fraudulent activities.
Role Overview: We are looking for a seasoned Product Manager to lead our Payment Protection offering. This role focuses on providing our customers with Risk Score AI tools to fight fraud. You will leverage advanced machine learning and real-time data analysis to detect and block fraudulent activities across the customer journey, including fraudulent account creations, use of stolen credit cards, suspicious account updates, fraudulent orders and transactions, chargebacks, and other abusive behaviors. Your goal is to maximize the use of ML for our customers’ benefit, providing a delightful UX for analyst investigations and fraud ring detection, and capabilities allowing to automate these tasks.
Key Responsibilities:
Customer-Centric Approach: Engage regularly with customers, validate ideas through feedback and data, and ensure customer needs are at the core of everything you do.
Own the Product Vision: Understand market offerings, identify unfulfilled customer needs, and bridge them. Own the “Why” and the “What” of the product, using data to back your assumptions and requirements.
Own the Product Roadmap: Take charge of the product roadmap, articulating both business and technical rationale behind your decisions, based on data insights.
Execute with Precision: Write detailed specifications, create tickets, draft copy, and work with the Product Marketing team to ensure comprehensive storytelling around your product.
Collaborate and Inspire: Communicate and motivate cross-functional teams, including engineering, product marketing, customer success, and sales, to align with your product vision.
Qualifications:
5-8 years of Product Management experience in the fraud prevention space
Deep understanding of ML modeling to identify fraudulent activities
Proven ability to convert customer requirements into successful product offerings and features
Experience collaborating closely with engineers, data scientists, engineering management, and designers
Comfortable working with Marketing and Sales teams to launch new product features and drive their adoption
Bonus Points:
Experience overseeing SaaS products
MBA or equivalent experience
Experience with fraud identity data and modeling
Benefits and Perks:
Competitive total compensation package
401k plan
Medical, dental, and vision coverage
Wellness reimbursement
Education reimbursement
Flexible time off
Interview Process:
Introduction interview with the recruiter (30 minutes)
Product strategy discussion with Senior Director of Product Management (45 minutes)
Technical interviews with Engineering Leaders (45 minutes)
Winning customer experience discussion with Chief Product Officer (30 minutes)
Values and behavior-based interview with Senior Director, People Partner (45 minutes)
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Brands including DoorDash, Yelp, and Poshmark rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.
Let’s Build It Together
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around the way in which Sift handles personal data of job applicants: https://sift.com/recruitment-privacy
