Top Financial Services Machine Learning Engineer Jobs in New York City, NY

The intern will utilize machine learning and statistics to extract patterns from data, implement algorithms, back-test models, and communicate findings effectively.

The role involves developing and deploying personalized recommendation engines using advanced ML techniques, while collaborating with cross-functional teams to drive impactful customer experiences.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Lead the Portfolio Management and Loss Mitigation function in Risk Data Science, developing machine learning models, enhancing predictive power, and overseeing team performance.

Conduct ML and deep learning research to build, test, and deploy predictive models for trading. Collaborate with traders, researchers, and engineers to engineer features, preprocess data, prototype algorithms, and improve production research tooling to enhance trading performance.

The Senior Data & AI Engineer will develop and optimize data engineering and AI/ML solutions, collaborating with teams to modernize data systems and support risk management initiatives.

Lead end-to-end data science projects: build, deploy, and monitor ML models (credit risk, pricing, collections, fraud). Partner cross-functionally, productionize pipelines, write production-grade code, and communicate results to technical and non-technical stakeholders.

The Senior Machine Learning Platform Engineer will build foundational components for ML models, mentor junior engineers, and optimize streaming pipelines. Responsibilities include enhancing ML infrastructure and ensuring data quality.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Design and deploy machine learning solutions for credit risk and fraud management, collaborating with stakeholders to optimize model performance and maintain governance of the model lifecycle.