About DataVisor:
DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.
Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!
Role Summary
We are seeking a hands-on Senior Data Scientist to serve as the "Architect of Efficacy" for our AI-Powered Fraud 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.
Responsibilities
- 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.
Requirements
Qualifications
- Experience: 1–5 years 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).
Preferred Qualifications
- 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
- Total Compensation: Includes Base + Performance Bonus + Equity Options.
- Benefits:
- Comprehensive medical, dental, and vision coverage.
- 401(k) retirement plan.
- Flexible Time Off (FTO) and paid holidays.
- Opportunities for R&D exploration and professional development.
- Regular team-building events and a collaborative, innovative culture.
