Responsibilities:
Lead the development and optimization of spam and fraud detection systems using machine learning and rule-based engines.
Work closely with management, product, engineering, compliance, legal, and security teams to align, present, and implement end-to-end fraud solutions.
Ensure adherence to KYC, AML, and other financial regulations while managing fraud risks.
Leverage the extensive data received from our application to enhance model performance and accuracy.
Requirements:
7+ years of experience in hands-on working on spam and fraud problems at a scale of at least 1MM transactions/events per day, deployed solutions to production with a proven impact.
3+ years of experience in managing Spam, fraud prevention and risk teams at a corporation, preferably in fintech or cyber-tech companies with a proven business impact.
3+ years of experience in Python, SQL, and AWS cloud. Ability to write readable and maintainable code.
Masters degree in Statistics, Finance, Data Science, or Computer Science.
Advantages:
Background in data science or cybersecurity with a focus on spam and fraud.
Proven track record of reducing fraud and spam actors at scale, with strong type-1 and type-2 error estimates.
Strong understanding of hypothesis testing and RCT. Background in applied statistics.
Working in high scales of >10MM transactions/day.
Experience working with technologies like Athena/Trino, Spark, AML, CI/CD, and Tableau.
