Senior Fullstack Developer - Scam Prevention

Description

You will join the Scam Prevention Team, which protects the platform from sophisticated financial crime.

The team builds machine learning, real-time transaction monitoring and data analysis tools to create controls and detection capabilities. The aim is a globally scalable fraud prevention engine that protects customer accounts and prevents scams while exceeding regulatory expectations.

This role focuses on continuously improving the company's financial crime prevention infrastructure and systems. You will own projects end to end and see direct, measurable results for customer security.

The company is a global technology business that helps people and businesses move and manage money across borders, including sending money abroad, spending overseas and making or receiving international payments.

Responsibilities

• Build and refine critical controls that stop scams targeting customers.
• Work on the backend of scam prevention and detection systems, about 80% of your time, and on the frontend, about 20%.
• Protect customers' money, speed up product growth by raising automation levels, and help build a best-in-class money transfer product.
• Take end-to-end ownership of projects and make data-driven decisions with the customer in mind.

Requirements

• 6+ years of professional experience with Java and the Spring framework.
• Experience building and designing scalable web applications that handle high traffic, ideally with React-based frontends in JavaScript or TypeScript.
• Proven experience designing and running CI/CD pipelines, distributed systems and concurrent systems.
• Experience with relational and non-relational databases, including query optimisation and designing resilient schemas.
• Strong product mindset, accountability and the ability to drive projects on your own.
• Strong fundamentals, sharp problem-solving and a commitment to test-driven development and good coding practices.
• Clear communication of complex technical ideas to Product, Data Science and Investigations teams.
• Familiarity with the machine learning lifecycle, including data pipelines, feature engineering and deploying ML systems to production.

Conditions

Equal opportunity: the company values diverse, equitable and inclusive teams made up of people from many countries.