Top Fintech Machine Learning Engineer Jobs in New York City, NY

As a Manager, Data Scientist, you will design and implement machine learning models to enhance payment experiences, utilizing large-scale data analytics and collaborating with cross-functional teams.

Lead a team to develop machine learning models for credit decisions, collaborating with cross-functional teams to drive data insights.

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 machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

Lead the design and development of scalable machine learning systems, mentor data scientists, and influence product roadmaps to enhance customer experience and business outcomes.

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, build, and scale production ML systems (including LLMs) to automate customer support and compliance workflows. Lead technical roadmaps, write high-quality Python code, mentor engineers, ensure secure, scalable architectures, and resolve complex reliability issues.

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.