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 own the detection strategy behind our AI-powered Fraud and AML Solutions suite. You will design the logic that decides what gets flagged — typologies, segmentation, thresholds, and false-positive tradeoffs — across Real-Time Payments (RTP), ACH, Wire, Check, and Application/Onboarding. You will also solve the industry-wide "Cold Start" problem: designing detection that protects new clients from day one, before their historical data is fully integrated. Working at the intersection of fraud domain expertise and data, you will partner closely with our Product, Strategy, Data Science, Delivery, and Engineering teams to translate how fraud actually happens into scalable, automated defenses. This is a strategy and analytics role — you will work in Python and SQL every day, but the core of the job is judgment about risk. Responsibilities • Design Pre-Built Detection Strategies: Build, back-test, and tune the detection strategies powering our core solution modules — Real-Time Payments (RTP), ACH, Wire, Check, and Application/Onboarding — balancing catch rate against customer friction. • Translate Typologies into Detection: Turn fraud and money-laundering typologies — synthetic identity, account takeover, scams, mule networks, structuring, check kiting — into concrete, testable detection logic. • Build the Global Consortium: Help design and stand up DataVisor's cross-industry fraud intelligence network — defining which signals to collect, how to normalize them across clients, and how to turn shared device and behavioral data into detection that generalizes. • Solve "Cold Start": Design generalized detection strategies that deliver immediate value to new clients, protecting them against known threats 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, reviewing automated fraud detection logic for soundness, transparency, and false-positive impact. • Partner Cross-Functionally: Work with Product, Strategy, Data Science, Delivery, and Engineering teams to take detection strategies from concept to production. Qualifications • Education: BS or MS in Statistics, Mathematics, Economics, Computer Science, Engineering, or a related discipline. • Experience: Minimum 1 year of hands-on experience in fraud strategy, AML/financial crime, risk analytics, data science, or a closely related field. • Domain Knowledge: Working understanding of fraud or AML typologies and payment rails (FedNow, RTP, ACH, Wire). • Technical Core: Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL. Both are assessed in our technical screen. • Analytical Rigor: Solid foundation in performance evaluation and tradeoff analysis — precision/recall, AUC, KS, false-positive rates, alert volumes, and catch rate. Preferred Qualifications • Experience owning fraud or AML strategy at a bank, credit union, fintech, or platform. • Familiarity with rules engines, case management systems, or alert-tuning workflows. • Exposure to unsupervised learning, anomaly detection, or graph/link analysis — as a consumer of these methods, not necessarily a builder. • Previous experience working in a high-growth SaaS or Fintech environment. • 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. • Discretionary Time Off (DTO) plus paid holidays. • Opportunities for research, development, and professional advancement. • Regular team-building events in a collaborative and innovative work environment.

Requirements

Must-haves

Translate Typologies into Detection: Turn fraud and money-laundering typologies — synthetic identity, account takeover, scams, mule networks, structuring, check kiting — into concrete, testable detection logic.
Solve "Cold Start": Design generalized detection strategies that deliver immediate value to new clients, protecting them against known threats 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, reviewing automated fraud detection logic for soundness, transparency, and false-positive impact.
Partner Cross-Functionally: Work with Product, Strategy, Data Science, Delivery, and Engineering teams to take detection strategies from concept to production.
Education: BS or MS in Statistics, Mathematics, Economics, Computer Science, Engineering, or a related discipline.
Experience: Minimum 1 year of hands-on experience in fraud strategy, AML/financial crime, risk analytics, data science, or a closely related field.
Domain Knowledge: Working understanding of fraud or AML typologies and payment rails (FedNow, RTP, ACH, Wire).
Technical Core: Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL. Both are assessed in our technical screen.
Analytical Rigor: Solid foundation in performance evaluation and tradeoff analysis — precision/recall, AUC, KS, false-positive rates, alert volumes, and catch rate.

Nice-to-haves

Experience owning fraud or AML strategy at a bank, credit union, fintech, or platform.
Familiarity with rules engines, case management systems, or alert-tuning workflows.
Exposure to unsupervised learning, anomaly detection, or graph/link analysis — as a consumer of these methods, not necessarily a builder.
Previous experience working in a high-growth SaaS or Fintech environment.
Salary ranges between USD 120,000 and 170,000.
Total compensation includes base salary, performance bonuses, and equity options.
Discretionary Time Off (DTO) plus paid holidays.
Opportunities for research, development, and professional advancement.
Regular team-building events in a collaborative and innovative work environment.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpythonsql

Platform: workable
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)