Data Scientist, AI Solutions

DataVisor; United States; On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Salary range: USD 120,000–170,000 per年

Equity options

Medical, dental, vision insurance

401(k) retirement plan

Flexible Time Off

Job summary

DataVisor, the world’s leading AI-powered Fraud and Risk Platform, seeks a hands-on Data Scientist to architect efficacy for detection solutions. You will design pre-built strategies, work across Product, Strategy, DS, Delivery, and Engineering, and translate fraud patterns into scalable defenses.

You will collaborate to implement advanced ML/LLM capabilities, address Cold Start issues, and ensure high-quality, real-time protection for new clients.

Qualifications

• MS or MS in CS, Stats, Math, Engineering, or related field.
• At least 1 year of hands-on DS or Advanced Analytics.
• Strong Python (Pandas, NumPy, Scikit-learn) and SQL skills.
• Solid foundation in statistical modeling and performance metrics (Precision/Recall, AUC, KS).

Responsibilities

• Develop Pre-Built Detection Models: design, back-test, and optimize statistical baselines and ML strategies for RTP, ACH, Wire, Check, and Onboarding.
• Mine the Global Consortium: analyze large-scale cross-industry data to identify high-risk patterns and create reusable features.
• Architect Cold Start Logic: create generalized scoring models to protect new clients before historical data is integrated.
• Validate AI Agent Logic: test and validate automated fraud detection logic for safety and low false positives.
• Cross-Functional R&D: collaborate to explore state-of-the-art ML and LLM capabilities and productionize concepts.

Skills

Python

Statistical analysis

Machine learning concepts

Education

MS in CS/Stats/Math/Engineering

Tools

SQL

Pandas

NumPy

Scikit-learn

Job description

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 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.

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.

Qualifications

• 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).

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

• Salary ranges between USD 120,000 and 170,000.
• Total compensation includes base salary, performance bonuses, and equity options.
• 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.