Top AI & Machine Learning Jobs in New York City, NY

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

Manage and grow a team of data scientists building end-to-end fraud detection models and products. Provide technical mentorship, lead cross-functional planning and communication, develop production-ready models and code, research new fraud types, and deliver data-driven insights to inform product, operations, and business strategy.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Build, train, and productionize machine learning models for fraud detection and identity verification. Perform data acquisition, feature engineering, experimentation, monitoring, and analyses to inform product, sales, and risk decisions. Collaborate with engineering, data acquisitions, and risk operations to maintain data quality and ship production-ready code. Research new fraud types and contribute to new product development across the ML lifecycle.

Design, develop, and productionize machine learning models for fraud detection and identity verification. Perform end-to-end model lifecycle work including data acquisition, feature engineering, training, experimentation, deployment, and monitoring. Research new fraud types, build product-focused models, write production-ready code, and collaborate with engineering, operations, and data teams to support product and business needs.

Build and deploy production ML models for fraud and identity risk across the full ML lifecycle. Research new fraud types, engineer features, write production-ready code, collaborate with cross-functional teams, and inform product, data acquisition, and business decisions.

Build and productionize machine learning models for fraud and identity risk across the full ML lifecycle: data acquisition, feature engineering, training, deployment, monitoring, and analysis. Research new fraud types, collaborate cross-functionally, write production-ready code, and inform product, sales, and data acquisition decisions.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.