Associate Fraud Risk Data Scientist

Associate Fraud Risk Data Scientist Location: San Jose, CA (Hybrid) Local candidates preferred. Remote considered if no viable local talent is found. Experience Level: Mid-Senior (26 years) Education: Bachelors or Masters in Data Science, Mathematics, Statistics, Data Analytics, or related field Industry: Financial Services / eCommerce / Risk Analytics Relocation Assistance: No Visa Sponsorship: Not Available Contract Type: 1-Year Contract (covering multiple leaves); potential to extend based on performance and business need Work Schedule: MF, Day Shift (Pacific Time) Position Summary Seeking a highly motivated and technically skilled Associate Fraud Risk Data Scientist to support the Fraud Risk Data Science team. This role focuses on designing and deploying machine learning and AI solutions to detect and mitigate fraud across risk management functions. Youll collaborate closely with cross-functional stakeholders and use large datasets to drive actionable, scalable, and measurable fraud prevention solutions. Key Responsibilities Design and develop ML/AI models to detect and mitigate fraud risks. Analyze large datasets to extract insights and build predictive fraud models. Collaborate with product and engineering teams to implement and monitor models in real-time environments. Build dashboards and visualizations (Tableau, AWS QuickSight) to track key model performance indicators. Support AI/ML initiatives and projects across the risk management space. Deliver clear and compelling presentations of complex analytical findings to both technical and non-technical stakeholders. Drive transformation of risk analytics through AI innovation. Contribute to ongoing fraud loss mitigation strategy and reporting. Requirements 26 years of hands-on experience in: Fraud analytics, data science, or risk analysis in eCommerce, online payments, or product abuse environments. Machine learning, statistical modeling, and AI development frameworks. Bachelors or Masters degree in Data Science, Statistics, Computer Science, or related quantitative field Strong experience with: SQL (advanced query writing and optimization) Python (including libraries such as Pandas, NumPy, Scikit-learn) AWS (especially S3, Quicksight) Tableau or similar visualization tools Ability to manage large datasets and drive insights under ambiguity Excellent communication skills, especially translating technical findings to business stakeholders Experience collaborating with product and engineering teams to deploy real-time fraud detection models Preferred Skills LLM (Large Language Models) or AI tool implementation for risk/fraud use cases Experience with APIs, .NET, or advanced visualization platforms (e.g., Grafana, Power BI) Project management experience in data-driven environments Fraud detection experience using AI/ML in a production setting Interview Process Multiple Zoom interviews (23 rounds) Includes live SQL assessment in the first round Why You Should Apply If you're passionate about using data science and machine learning to solve real-world fraud challenges at scale, this is a rare opportunity to make a tangible impact from day one. Youll work on high-visibility projects in a fast-moving financial services environment with a team that values innovation, ownership, and clear results. Youll have the freedom to explore advanced AI tools (including LLMs), bring your own ideas to life, and shape the future of fraud preventionwhile enjoying a flexible hybrid schedule and potential long-term engagement. This role isnt just a contract its a chance to do meaningful, cutting-edge work that protects millions of users and helps shape smarter, safer digital finance. Apply now and be part of a forward-thinking team tackling some of the most complex and impactful challenges in fraud risk today.