Data Scientist, AI Solutions

DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. They are seeking a hands-on Data Scientist to build the mathematical core of their AI-Powered Fraud and AML Solutions suite, collaborating with various teams to develop detection strategies and validate AI-driven logic.

What you'll do:

• 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

What they're looking for:

• 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)
• Experience with graph theory or link analysis for detecting network-based fraud
• Familiarity with unsupervised learning techniques or anomaly detection
• Previous experience working in a high-growth SaaS or Fintech environment
• Domain Knowledge: Familiarity with Fraud Detection, Credit Risk, or Trust & Safety, including knowledge of payment rails (FedNow, ACH, Wire) and typologies (Synthetic ID, ATO, Kiting)

Benefits:

• Comprehensive medical, dental, and vision insurance coverage.
• 401(k) retirement savings plan available.
• Flexible Time Off (FTO) plus paid holidays.
• Opportunities for research, development, and professional advancement.
• Regular team-building events in a collaborative and innovative work environment.