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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist, Fraud Analytics - **Company:** Datavisor, Inc. - **Location:** United States - **Salary:** $120,000.0 - **Contract:** Internship / Graduate position - **Skills:** Software as a Service, Payment Systems, Graph Database, Intrusion Detection and Prevention, Python (Programming Language), Link Analysis, NumPy, BIG-IP Global Traffic Manager (GTM), SQL Databases, Large Language Models, Pandas, Scikit Learn, Information Technology, Unsupervised Learning - **Published:** September 24, 2026 - **Apply:** https://www.thejobnetwork.com/job/a227805b-f401-459a-a5a1-4ee0bdbdae2c/data-scientist-fraud-analytics ## About the Role * 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 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, catch rate. * Comfort Without Data: Able to propose a defensible approach when historical data is not yet available - which, for new clients, is the normal starting point. 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. * Experience working through AI tooling - using LLMs to accelerate research, analysis or documentation, and able to judge when the output is wrong. * Exposure to unsupervised learning, anomaly detection or graph/link analysis - as a consumer of these methods, not necessarily a builder. * Client-facing or consulting background. * Previous experience in a high-growth SaaS or Fintech environment. ## Description 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. Responsibilities * 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. ## Related Videos - [Detecting Money Laundering with AI](https://www.wearedevelopers.com/videos/111-detecting-money-laundering-with-ai) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Vectorize all the things! 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