About the role
The Risk Platform Engineering team is committed to reducing risk across Flex, our products, and for our customers, focusing on but not limited to areas like credit and fraud risk. Our mission is to enhance Flex app accessibility for customers while ensuring security by identifying and preventing misuse across all core product areas. Additionally, we play a key role in maintaining Flex’s positive lifetime value (LTV) by minimizing company losses through AI/ML solutions and effective risk policies.
We are seeking a Risk Domain Leader to join our team. In this role, you will not only be hands-on with coding, driving the development of platforms and APIs essential for risk management, but also lead a skilled team of backend and full-stack developers to build and enhance a scalable, best-in-class Risk Platform that supports Flex’s current and future risk products.
As an Engineering Manager in the risk domain, you will develop a comprehensive understanding of risk from company, product, and customer perspectives. You will align customer needs with our technical capabilities to deliver a strategic technical vision, influencing cross-functional partners to ensure that deliverables align with our north star goals in risk management while enhancing the quality of our platform.Additionally, you will support the professional growth of your engineers, mentor teammates in agile development practices, and work closely with partners to plan, prioritize, and deliver scalable and reliable products. You will drive DevOps best practices to ensure a robust incident response process and maintain a resilient test, build, and deployment pipeline.As a leader in the Risk Platform team, you will drive the following key projects and systems:
Core Decision Platform: Manage the end-to-end platform for business rule, analytics, and ML/AI-driven decision-making.
Event Data Stream: Support real-time data ingestion for rapid risk rule/policy setup.
A/B Testing and Experimentation: Enable advanced experimentation tools with adaptive algorithms for risk prevention.
Real-Time Fraud Monitoring: Deploy configurable systems to detect and respond to fraud in real-time.
ML and AI Microservices: Integrate ML/AI models as microservices for enhanced risk detection.
Qualifications
Minimum Qualifications
Minimum of 6 years in risk domains, with at least 3 years as a manager or tech lead.
8+ years of experience in software engineering, including 4+ years with Java.
Bachelor’s degree or higher in Computer Engineering or a related field.
Proficiency in Java frameworks and tools used at Flex: Spring (core/web/boot), Gradle, JUnit, and JVM optimization.
Expertise in Service-Oriented Architecture, REST APIs, Message Queues, and scalable architecture.
Familiarity with AWS tools (EKS, Aurora RDS, Elasticache, DynamoDB) and containerization.
Strong experience in delivering high-impact products end-to-end, with a focus on quality and timeliness. You are able to think rigorously and make hard decisions and tradeoffs
Ability to make analytical, data-driven decisions and recover quickly from setbacks.
Strong written and verbal communication skills, with mentoring experience across various seniority levels.
Preferred Qualifications
Extensive experience in adversarial domains such as Credit Risk, Fraud, Trust, or Safety.
Hands-on experience in building, designing, or managing decision platforms or rules engine architectures within the risk domain.
Proven track record in developing Data and ML/AI systems tailored to risk management.
Strong familiarity with big data platforms and tools like Snowflake for scalable data processing and analysis.
Proficiency in observability and monitoring tools (e.g., DataDog) to maintain high system availability and reliability.
The salary compensation range for this role will be commensurate with the candidate's experience and Flex's internal leveling guidelines and benchmarks.
For working locations in NY/NJ/CA, the base salary pay range will be $221,000-$237,000. For all other states, the base salary pay range will be $199,000-$213,000
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