Description
In this role, you will serve as a hands-on individual contributor. You will help shape the technical direction of the team and build long-term, firmwide capabilities to identify and prevent fraud, leveraging cutting-edge techniques and modern cloud-based tools in an AWS setting.
Job Responsibilities:
- Develop, train, and deploy machine learning models for fraud prevention and risk management.
- Research and implement novel architectures, including Graph Networks, Agentic AI, and Large Language Models.
- Build and test AI agents, iterating designs to enhance functionality and user experience. Conduct rigorous testing to ensure reliability and effectiveness of AI solutions.
- Use tools like Databricks and PySpark to create data pipelines and dashboards that support AI-driven insights and decision-making.
- Monitor and optimize model performance in real-world environments, adapting to evolving fraud patterns.
- Lead technical strategy and guide analytical direction within the team, fostering a culture of innovation and continuous improvement.
- Mentor and support junior team members, sharing best practices and technical expertise.
- Collaborate with cross-functional teams—including product, engineering, and data science—to align modeling solutions with business objectives and firmwide priorities.
- Contribute to the development of scalable, reusable machine learning solutions and best practices that strengthen the firm’s overall fraud prevention capabilities.
Required Qualifications, Capabilities, and Skills:
- Master’s degree in Computer Science, Mathematics, Statistics, Economics, or a related quantitative field, or equivalent work experience.
- Minimal 5-year of experience in developing and managing predictive risk models in financial institutions.
- Deep understanding of machine learning theory and algorithms, with hands-on experience in both classical and deep learning methods.
- Proficient in Python, SQL or PySpark with experience in deep learni