Associate Fraud Risk Data Scientist
Listed on 2025-12-23
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IT/Tech
Data Scientist, Data Analyst
San R&D Business Solutions LLC | Contract
San Jose, United States | Posted on 10/08/2025
Now Hiring: Associate Fraud Risk Data Scientist | Hybrid
Work Location: San Jose, CA
Work type: Contract (W2 only)
Work Authorization: Only GC or US Citizens
About the RoleWe are seeking a talented and driven Fraud Risk Data Scientist to join our Risk Data & AI Innovation team. In this role, you will design and deploy AI/ML models to detect fraud, mitigate risk, and reduce losses across eCommerce and payments ecosystems. You’ll collaborate with product, engineering, and business stakeholders to develop scalable, real-time fraud detection solutions while driving AI transformation within risk management.
This is an exciting opportunity for someone passionate about combining data science, machine learning, and business impact in the fraud and risk domain.
Key Responsibilities- Design, develop, and implement machine learning and AI models for fraud detection and risk mitigation.
- Support stakeholders in effectively leveraging models for risk decision-making.
- Partner with product and engineering teams to deploy and monitor models at scale in real-time environments.
- Conduct statistical analysis and data science to solve complex fraud and risk problems.
- Build dashboards and visualizations (e.g., Tableau) to track KPIs and model performance.
- Communicate insights, results, and recommendations clearly to technical teams, business partners, and executives.
- Drive AI transformation across risk management activities, including experimenting with LLMs and other emerging AI tools.
Skills & Qualifications
- 2–6 years of experience in machine learning/AI, data science, risk analytics, and data analysis
. - Relevant industry experience in eCommerce, online payments, user trust/risk/fraud, or product abuse investigation
. - Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Data Analytics, or related field
, or equivalent practical experience. - Proficiency in Python, SQL, AWS, Excel and key data science libraries.
- Strong experience working with large datasets
. - Proficiency in data visualization tools (Tableau preferred).
- Strong analytical and problem-solving skills with the ability to make data-driven recommendations.
- Experience developing and implementing AI tools such as LLMs for risk use cases.
- Hands-on experience in dashboard creation, model monitoring, and KPI reporting.
- Prior experience in fraud detection or risk-related data science applications.
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