Building Revenue Protection Through Ai

1 week ago

Brisbane, Queensland, Australia Bebeefraud Full time

Machine Learning Fraud Prevention Specialist
The position of Machine Learning Fraud Prevention Specialist is a key role within our revenue protection team, focused on developing and deploying fraud detection models to prevent financial losses across various business channels. This position requires a strong foundation in machine learning, data science, and software engineering.
Developing and deploying machine learning models in production environments for fraud, abuse, or anomaly detection use cases.
Collaborating with backend engineers, data scientists, and product managers to build and iterate on revenue protection strategies.
Driving the development of our fraud ML strategy using a strong product + platform mindset.
Designing, building, and maintaining ML systems that underpin revenue detection and response.
Mentoring and upskilling engineers and data scientists in the team to support model development.
This individual should have hands-on experience building complex internal tools and user-facing flows at scale, and be comfortable navigating ambiguous problem spaces with incomplete data and evolving priorities.
Key qualifications include:
A strong grasp of core CS fundamentals, system design, and architecture.
Proficiency in Python and familiarity with tools like PyTorch, TensorFlow, or scikit-learn.
Experience in fintech and business process automation is highly valued.
As part of our platform team, we work closely with other teams around the company to achieve their goals and maintain the integrity of our business operations. Our systems affect millions of users, numerous products, and every transaction processed by our company.
If you have a passion for machine learning, revenue protection, and working in a fast-paced environment, we encourage you to apply. We celebrate all types of skills and backgrounds, so even if your skills don't perfectly match the job description, we still want to hear from you.

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