Machine Learning Engineer

About This Role

Architect and develop advanced machine learning systems for fraud prevention, abuse mitigation, and inclusive credit decisioning for a financial technology company. Leverage large datasets and sophisticated analytical techniques to enhance the resilience and accuracy of the lending ecosystem. This role will influence risk management strategies and pioneer AI-native, agentic risk systems.

Responsibilities

• Design and develop next-generation lending decision frameworks utilizing advanced analytics, machine learning, and agentic automation to improve precision, speed, and adaptability
• Analyze large, complex datasets to uncover insights that influence underwriting strategies, strengthen ecosystem safeguards, and guide product development
• Lead signal discovery efforts and conduct deep experimentation to identify evolving risk patterns and inform the design of robust risk management systems
• Collaborate with Product, Engineering, and Operations teams to create systems and features that enhance customer trust and maintain portfolio health
• Mentor and develop fellow data scientists and modelers, establishing modeling standards, best practices, and technical excellence across the team
• Drive solutions from problem framing and design through deployment, iteration, and continuous improvement with a high degree of autonomy and ownership
• Stay ahead of emerging fraud vectors and drift in data by developing self-adapting AI-native systems that proactively mitigate risks

Requirements

• 10+ years of industry experience in risk management, fraud detection, credit underwriting, or high-stakes decisioning domains
• Deep expertise in machine learning methods, statistical modeling, and advanced analytics
• Proven experience designing and validating automated models and decision logic frameworks
• Strong analytical skills with a rigorous testing and validation mindset
• Excellent communication skills, capable of translating complex technical concepts to diverse stakeholders
• Hands-on experience with agentic workflows, including LLM-based agents or automation components that enhance modeling velocity and decision accuracy
• Proficiency in SQL, Python (including NumPy, Pandas, Sklearn, TensorFlow), GitHub, and cloud platforms such as GCP or AWS

Qualifications

• Bachelor's degree in Mathematics, Statistics, Physics, Computer Science, or a related quantitative field
• 10+ years of industry experience in risk management, fraud detection, credit underwriting, or high-stakes decisioning domains

Nice to Have

• Master’s degree or PhD

Skills

* Required skills

Benefits