Data Scientist, Fraud Analytics

Data Visor 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, Data Visor'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.

Data Visor'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. Data Visor 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!

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 available.

This is a strategy and analytics role, and it is also a hands-on solutions role. Alongside designing detection, you will configure the platform that runs it, stand up tenants, write the documentation clients and colleagues rely on, and answer questions from teams across the company. You will work in Python and SQL every day, but the core of the job is judgment about risk.

• Design Pre-Built Detection Strategies:
  Build, back-test and tune the strategies powering our core solution modules — 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.
• Solve "Cold Start":
  Design generalized detection that delivers immediate value to new clients, protecting them against known threats before their historical data is available.
• Configure the Platform:
  Set up and tune what makes detection usable — case manager review queues, alert detail layouts, knowledge graph and investigation lists — and stand up tenants for internal and client demos.
• Write for Clients and Colleagues:
  Produce the technical documentation, integration notes and solution write-ups that clients and internal teams work from.
• Answer the Business:
  Handle incoming questions from GTM, Solution Engineering, Customer Support and Technical Account Management — research the answer, with or without data, and write it up.
• Partner Cross-Functionally:
  Work with Product, Strategy, Data Science, Delivery and Engineering to take detection strategies from concept to production.

• Education:
  
  BS or MS in Statistics, Mathematics, Economics, Computer Science, Engineering, or a related quantitative discipline. A master's is preferred, not required.
• Experience:
  
  At least 1 year of full-time professional experience in fraud strategy, AML/financial crime, risk analytics, data science or a closely related field. Internships are not counted toward this minimum.
• Domain…