DataVisor
Finance • Fintech • SaaS
DataVisor is an advanced fraud and risk management platform leveraging AI and machine learning to provide real-time solutions for financial institutions and other large organizations. The platform offers a comprehensive suite of tools to tackle various types of fraud, including account takeovers, application fraud, ACH and wire fraud, card fraud, and check fraud. DataVisor also provides solutions for AML (Anti-Money Laundering) compliance, helping banks, credit unions, fintech companies, and digital payment services detect and prevent fraudulent activity. Through its innovative machine learning algorithms and real-time data orchestration, DataVisor allows organizations to streamline their operations, reduce fraud losses, increase approval rates, and maintain compliance, all while protecting the integrity of their systems and user data. The company emphasizes quick and efficient integration with existing systems and provides educational resources to stay ahead of emerging threats, making it a valuable partner for modern financial operations.
Senior Data Scientist – Fraud Detection
Job not on LinkedIn
October 7
DataVisor
Finance • Fintech • SaaS
DataVisor is an advanced fraud and risk management platform leveraging AI and machine learning to provide real-time solutions for financial institutions and other large organizations. The platform offers a comprehensive suite of tools to tackle various types of fraud, including account takeovers, application fraud, ACH and wire fraud, card fraud, and check fraud. DataVisor also provides solutions for AML (Anti-Money Laundering) compliance, helping banks, credit unions, fintech companies, and digital payment services detect and prevent fraudulent activity. Through its innovative machine learning algorithms and real-time data orchestration, DataVisor allows organizations to streamline their operations, reduce fraud losses, increase approval rates, and maintain compliance, all while protecting the integrity of their systems and user data. The company emphasizes quick and efficient integration with existing systems and provides educational resources to stay ahead of emerging threats, making it a valuable partner for modern financial operations.
📋 Description
• Lead the full lifecycle of fraud detection features and models, from ideation and data exploration to prototyping, productionizing, and monitoring. • Develop highly predictive features from complex, large-scale, multi-dimensional data, including user behavior, device intelligence, network graphs, and transaction records. • Research, design, and implement state-of-the-art machine learning algorithms, combining supervised, unsupervised, and semi-supervised techniques to detect novel and evolving fraud patterns. • Work with massive, noisy, and imbalanced datasets (billions of events) using tools like Spark, SQL, and our proprietary AI platform. • Partner closely with Engineering to ensure robust, low-latency model deployment and with Product Management to translate complex client needs into technical solutions. • Conduct deep-dive analyses on fraud attacks, extract actionable insights, and translate them into improved detection strategies and rules. • Provide technical guidance and mentorship to junior data scientists, fostering a culture of excellence and continuous learning.
🎯 Requirements
• Master's or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field. • 5+ years of professional experience in data science, with a significant focus on fraud detection, cybersecurity, or a related adversarial domain. • Deep, hands-on experience with machine learning lifecycle in a production environment. • Strong programming skills in Python (must-have) and proficiency with SQL. Experience with PySpark is a significant plus. • Solid understanding of both classic machine learning models (Logistic Regression, Gradient Boosting, etc.) and modern techniques (Deep Learning, Graph Neural Networks). • Proven experience with feature engineering and a keen intuition for what makes a feature predictive and robust in a dynamic environment. • Experience with large-scale data tools (Spark, Hadoop, etc.) and cloud platforms (AWS, GCP, Azure). • Professional proficiency in written and spoken English, with the ability to collaborate effectively in a global, cross-functional team. • Excellent communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences. • Based in Japan
🏖️ Benefits
• PTO
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