We're in search of an exceptional hands-on Data Scientist who can bridge data infrastructure, fraud analytics, and machine learning to enhance our fraud detection capabilities. This role requires someone who can drive projects from ideation to implementation, leading these efforts while thinking strategically about how we detect and prevent fraud at scale. Our ultimate goal is to equip our clients with resilient safeguards against chargebacks, empowering them to safeguard their revenue and optimize their profitability. Join us on this thrilling mission to redefine the battle against fraud.
Your Arena
Develop and enhance fraud detection models from concept to production, focusing on data normalization, network analysis, and risk scoring.
Take ownership to identify, analyze, and implement solutions for fraud patterns and behavioral anomalies.
Design, build and maintain data science pipelines and fraud intelligence systems to improve detection accuracy.
Collaborate with product, engineering, and risk teams to implement fraud prevention strategies.
Own projects end-to-end – balancing short-term wins with long-term strategy.
Your Arena
Develop and enhance fraud detection models from concept to production, focusing on data normalization, network analysis, and risk scoring.
Take ownership to identify, analyze, and implement solutions for fraud patterns and behavioral anomalies.
Design, build and maintain data science pipelines and fraud intelligence systems to improve detection accuracy.
Collaborate with product, engineering, and risk teams to implement fraud prevention strategies.
Own projects end-to-end – balancing short-term wins with long-term strategy.
Requirements:
4+ years of hands-on experience in fraud analytics, data science, or risk modeling – Must
Proven experience in the Fintech/Fraud Prevention Domain – Must
Proven ability to take projects from A to Z in building data infrastructure, anomaly detection, and graph-based fraud detection solutions – Must
Strong practical proficiency in Python, SQL, MLOps and fraud-related data tools – Must
Strong background in e-commerce or payments – Advantage
A self-starter mentality – someone who identifies opportunities, takes initiative, and drives improvements with minimal direction.
4+ years of hands-on experience in fraud analytics, data science, or risk modeling – Must
Proven experience in the Fintech/Fraud Prevention Domain – Must
Proven ability to take projects from A to Z in building data infrastructure, anomaly detection, and graph-based fraud detection solutions – Must
Strong practical proficiency in Python, SQL, MLOps and fraud-related data tools – Must
Strong background in e-commerce or payments – Advantage
A self-starter mentality – someone who identifies opportunities, takes initiative, and drives improvements with minimal direction.
This position is open to all candidates.
