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Product Manager

• Define and champion the long-term vision, strategy, and roadmap for the risk product platform, aligning with company and product objectives.
• Own the end-to-end product lifecycle of the orchestration platform of shared platform services, including fraud detection and risk mitigation solutions, income verification services and other third-party decisioning services.
• Drive development of API-based services (advanced income verification, fraud detection), ensuring robustness, scalability, and reusability across product portfolio.
• Lead strategic direction and development of the Policy Optimizer product, leveraging ML and AI to enhance decision-making and platform performance.
• Lead, mentor, and grow a small team of Product Managers specializing in Data and Fraud.
• Partner with engineering, architecture, data science, risk, legal, compliance, and other product teams to define requirements, prioritize initiatives, and ensure successful delivery.
• Manage key relationships with external vendors and third-party service providers.
• Utilize data, market research, and competitive analysis to inform product decisions and measure success of platform initiatives.
• Communicate platform strategy, roadmap, and value proposition to executive leadership and cross-functional teams.
• Reports to SVP of Product.

Director of Product Management – Risk Platform

QA Automation Engineer

• Design and develop scalable automation frameworks from scratch using Java/Python and other open-source tools
• Implement automated test scripts for functional and regression testing
• Integrate automated tests with continuous integration/continuous deployment (CI/CD) pipelines
• Collaborate with QA engineers, developers, and stakeholders to ensure high-quality delivery of software products
• Troubleshoot and debug automation failures in a timely manner
• Maintain and enhance existing automation frameworks and test suites
• Stay updated on industry best practices and new technologies to improve testing efficiency
• Be proactive in identifying areas for improvement and optimizing existing systems for performance and reliability

Automation Engineer

Full-stack Engineer

• Lead a team of engineers, fostering collaboration, mentorship, and technical growth
• Write clean, maintainable, and scalable full-stack code meeting performance, reliability, and security standards
• Translate business requirements into intuitive front-end experiences and robust back-end services
• Improve application performance, scalability, and maintainability across the stack
• Drive integration between React front-end applications and Java/Spring Boot back-end systems
• Conduct code reviews, set technical direction, and promote a culture of continuous improvement
• Partner closely with product and design teams to align technical execution with user needs and business goals
• Troubleshoot complex issues, make architectural decisions, and help the team deliver performant, scalable fintech solutions
• Review pull requests, mentor teammates, and refine sprint goals; lead and collaborate with a group of 5+ engineers

Lead Software Engineer

Data Scientist

• Take ownership of model development, selection, and optimization with a focus on fraud and risk
• Oversee, maintain, and evolve in-house models using machine learning techniques
• Strong product bias with significant client-facing component to maximize product performance
• Model Development, Management & Evolution: monitor performance, identify improvements, implement enhancements with advanced ML algorithms
• Fraud Prevention: lead evolution of core fraud prevention capabilities, conduct build vs. buy analyses, assess third-party fraud models
• Model Governance: ensure models are governed, respond to model validation requests, explain functionality, provide performance and compliance evidence
• Policy Optimizer Analysis: leverage statistical methods to help clients configure credit policies and optimize underwriting rules
• Customer Success: work with customers to evolve and maximize credit and fraud policies via regular meetings and insights
• Cross-Functional Collaboration: partner with Product, Engineering, and Customer Success to integrate and optimize models
• Data-Driven Insights: analyze large datasets to uncover trends, identify risks, and find opportunities for product innovation

Director of Product Management – Risk Platform

Automation Engineer

Lead Software Engineer

Director of Fraud, Risk, and Data Science