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Machine Learning is an integral part of how we at Cash App design products, operate, and pursue our mission to serve the unbanked as well as disrupt traditional financial institutions. Our massive scale and deep trove of transaction data create an endless number of opportunities to use ML and AI methods to better understand our customers and offer new products and experiences that can improve their lives. We are a highly creative group that prefers to solve problems from first principles; we move quickly, make incremental changes, and deploy to production every day. As part of the Risk ML team, you will help shape the future of teen banking by developing models that enable safe and trustworthy financial experiences for teens and their parents. Your work will focus on building machine learning systems that detect and prevent risky or abusive activity, strengthen account integrity, and ensure a secure ecosystem for families on Cash App. You'll experiment with state-of-the-art algorithms to identify emerging risk behaviors, detect potential abuse in real time, and help ensure that millions of customers can use Cash App safely and confidently.
Job Responsibility:
Build machine learning models to detect and act against risky or abusive activity across the Cash Families product
Collaborate cross-functionally with engineering, product, and operations teams to design systems and features to protect and maintain trust with our customers
Work closely with the ML Engineering teams who build the systems that allow our models to operate at scale and in real time
Research emerging risks and behavioral patterns in financial activity to proactively shape safeguards and policy
Contribute to the growth of our modelling capabilities through mentoring and supporting fellow modellers
Exercise a high level of autonomy and responsibility, owning your solutions from design through to operation
Requirements:
Bachelor's degree in a quantitative field such as Mathematics/Statistics/Physics or Machine Learning. Masters or PhD preferred
5+ years of experience in machine learning, artificial intelligence, or a related field
Strong knowledge of machine learning algorithms and data analysis techniques
Excellent problem-solving skills and attention to detail
Strong communication skills, with the ability to explain complex concepts to non-technical stakeholders