Machine Learning Engineer (Risk)

SHIELD is a device-first fraud intelligence platform that helps digital businesses worldwide eliminate fake accounts and stop all fraudulent activity.

Powered by SHIELD AI, we identify the root of fraud with the global standard for device identification (SHIELD Device ID) and actionable fraud intelligence, empowering businesses to stay ahead of new and unknown fraud threats.

We are trusted by global unicorns like inDrive, Alibaba, Swiggy, Meesho, TrueMoney, and more. With offices in LA, London, Jakarta, Bengaluru, Beijing, and Singapore, we are rapidly achieving our mission - eliminating unfairness to enable trust for the world.

Responsibilities

As a Machine Learning Engineer (Risk), you will develop and leverage innovative machine learning solutions to solve complex sets of problems and applications. By analyzing and detecting patterns in vast amounts of data, you will have a good understanding of machine learning life cycle-algorithms, data structures and design patterns. We are looking for talented and passionate individuals who are proactive in identifying problems and have the logical thought process and skills to solve them. There will be many opportunities to explore new tech stacks and to work on advanced technologies.

• Ability to design and develop machine learning algorithms
• Discover, design, and develop analytical methods to support novel approaches of data and information processing
• Identify and apply appropriate methods to process and analyze large data-sets of labelled and unlabeled records, and discover new valuable insights for the system
• Provide support on other part of the system (not limited to Machine Learning)
• Conduct software performance analysis, scaling, tuning and optimization
• Review and contribute to improve current software and system architecture for stability and to optimize performance
• Research & development of fraud detection solution

Requirements