Vice President - Applied AI/ML Scientist - Fraud & Risk Analytics

About the job

Hiring: Vice President - Applied AI/ML Scientist - Fraud & Risk Analytics Are you passionate about building AI systems that make real-time decisions on live financial transactions? Do you thrive at the intersection of research and production? We’re looking for a senior Applied AI/ML Scientist to help shape the future of fraud prevention and digital payments security. This is a high-visibility, high-impact role where your models will directly reduce fraud losses, influence firmwide strategy, and power a scalable fraud prevention platform used across the organization. You will: • Design, train, and deploy advanced machine learning models for fraud prevention and risk management • Research and implement cutting-edge architectures, including: Graph Networks, Agentic AI systems, Large Language Models (LLMs) • Build and rigorously test AI agents to ensure reliability, robustness, and real-world effectiveness • Develop scalable data pipelines and analytical tools using Databricks, PySpark, and AWS • Monitor, optimize, and continuously evolve models to adapt to emerging fraud patterns • Drive technical strategy and influence the analytical direction of the team • Mentor junior scientists and promote engineering and modeling best practices • Partner cross-functionally with Product, Engineering, and Data teams to align AI solutions with business impact • Build reusable, production-grade ML frameworks that elevate firmwide fraud prevention capabilities What You Bring • Master’s degree (or equivalent experience) in Computer Science, Statistics, Mathematics, Economics, or related quantitative field • 10+ years of experience building and managing predictive risk models in financial institutions • Strong foundation in machine learning theory (not just library usage) • Hands-on experience with: • Python, SQL, and/or PySpark • PyTorch or TensorFlow • XGBoost, Scikit-learn, or similar classical ML tools • Experience working with large-scale datasets and distributed data processing • Experience in AWS cloud environments • Proven ability to take models from research → production → monitoring → optimization • Experience mentoring or coaching junior team members Nice to Have • Experience or strong interest in Graph Analytics and Agentic AI • Knowledge of GSQL • Experience working with both structured and unstructured data • Product mindset — you understand that models are part of a broader user and business experience • Passion for impact — your models making real-time financial decisions energizes you

Requirements

• Machine Learning
• AI Systems Development
• Python
• SQL
• PySpark
• AWS
• Data Pipelines
• Graph Networks

Qualifications

• Master’s degree in Computer Science
• Statistics
• Mathematics
• Economics

Preferred Technologies

• Machine Learning
• AI Systems Development
• Python
• SQL
• PySpark
• AWS
• Data Pipelines
• Graph Networks