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 Product Manager to lead our ML and ML Operationalization efforts. This role focuses on driving fraud risk score accuracy and stability, including our flagship capability, RiskWatch. Advocate for customer and business impact, while influencing and guiding the highly technical ML teams to deliver an easily explained measurable customer value. You will be responsible for ML model release and serving, as well as feature extraction. On the ML operationalization side, you will support model releases and work with customers on customer-specific calibration and ML integration.
Key Responsibilities:
Own Sift’s Machine Learning Vision: collaborate with Engineering Leaders to increase customer value and simplification of our ML
Oversee our Machine Learning outcomes: measure and monitor customer KPIs and advise ML research, development, and operations to improve said KPIs
Drive Fraud Risk Score Accuracy & Stability: Ensure the accuracy and stability of fraud risk scores, including managing RiskWatch.
ML Model Release and Servicing: Oversee the release and servicing of ML models, ensuring they meet performance and reliability standards.
Feature Engineering, Model Development, and Third-Party Data Integration: lead all aspects of enhancing model performance and help advocate and prioritize work that maximize customer impact.
Customer-Specific Calibration and ML Integration: Work closely with customers to calibrate models to their specific needs and integrate ML solutions into their systems.
Qualifications:
3-5 years of Product Management experience in the ML and fraud prevention space.
Strong and proven experience with ML and ML model releasing and serving, including ensuring data quality, and performance monitoring.
Track record in translating business and user experience metrics onto ML metrics.
Program management capabilities
Proven ability to manage ML model lifecycle and feature extraction
Experience collaborating closely with engineers, data scientists, and customers
Comfortable working with Marketing and Sales teams to launch new product features and drive their adoption
Bonus Points:
Proven experience at running complex programs while interfacing with customers
PMP or equivalent, proven experience
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.
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
