Data Scientist, DataVisor

• Education: MS or MS in Computer Science, Statistics, Mathematics, Engineering, or a related discipline
• Experience: Minimum 1 year of hands-on experience in Data Science or Advanced Analytics
• Technical Core: Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL
• Statistical Rigor: Solid foundation in statistical modeling, feature selection, and performance evaluation (Precision/Recall, AUC, KS)

Desirable

• We are seeking a hands-on Data Scientist to serve as the "Architect of Efficacy" for our AI-Powered Fraud and AML Solutions suite. In this role, you will move beyond simple analysis to build the mathematical core of our product.
• You will design pre-built detection strategies that provide immediate protection for new clients, solving the industry-wide "Cold Start" problem. Working at the intersection of research and product, you will collaborate closely with our Product, Strategy, Data Science, Delivery, and Engineering teams to translate complex fraud patterns into scalable, automated defenses
• Develop Pre-Built Detection Models: Design, back-test, and optimize statistical baselines and machine learning strategies for our core solution modules, including Real-Time Payments (RTP), ACH, Wire, Check, and Application/Onboarding
• Mine the Global Consortium: Analyze large-scale, cross-industry data within our global intelligence network to identify high-risk device fingerprints and patterns of organized fraud, transforming these insights into features that can be deployed across all clients
• Architect "Cold Start" Logic: Create generalized scoring models that deliver immediate value to new clients, ensuring they are protected against known threats even before their historical data is fully integrated
• Validate AI Agent Logic: Serve as the expert "Human-in-the-Loop" for our AI-driven strategy engine, rigorously testing and validating automated fraud detection logic to ensure safety, transparency, and low false positive rates
• Cross-Functional R&D: Collaborate with Product, Strategy, Data Science, Delivery, and Engineering teams to explore and implement state-of-the-art machine learning and large language model (LLM) capabilities, providing the statistical rigor needed to turn experimental concepts into production-grade features