Top AI & Machine Learning Jobs

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive modeling, forecasting, recommendation systems, fraud detection, and customer analytics. Defines experimentation frameworks, applies foundation-model embeddings, measures business impact, partners with cross-functional stakeholders, and mentors data scientists and AI engineers. Provides technical leadership, contributes to hiring and capability development, and shapes the organization’s long-term AI direction.

Lead machine learning and data science initiatives from problem definition through production deployment and business impact measurement. Develop predictive models, recommendation systems, forecasting, fraud detection, and customer analytics solutions using modern machine learning and foundation-model techniques. Establish experimentation and evaluation frameworks, partner with cross-functional stakeholders, present technical recommendations, mentor data scientists and AI engineers, and shape the organization’s technical direction.

Leads the design, development, deployment, and evaluation of machine learning solutions for forecasting, recommendation, fraud detection, risk modeling, and customer analytics. Defines modeling and experimentation frameworks, applies embeddings and foundation models, measures business impact, partners with cross-functional stakeholders, presents recommendations to leadership, and mentors data scientists and AI engineers. Contributes to hiring, capability development, and the organization’s technical direction.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive modeling, forecasting, recommendations, fraud detection, embeddings, and foundation-model applications. Defines experimentation frameworks and success metrics, drives projects from conception through production, partners with cross-functional stakeholders, presents technical recommendations, and mentors data scientists and AI engineers. Contributes to hiring, capability development, and the organization’s long-term technical direction.

Leads the design, development, deployment, and evaluation of machine learning solutions, including forecasting, recommendation, propensity, fraud, and customer analytics models. Defines experimentation frameworks and success metrics, applies foundation-model embeddings, partners with cross-functional stakeholders, presents technical recommendations, and mentors data scientists and AI engineers. The role also contributes to hiring, capability development, and the organization’s long-term AI direction.

Leads the design, development, deployment, and evaluation of advanced machine learning solutions, including forecasting, recommendation, propensity, fraud, and customer analytics models. Defines experimentation frameworks, applies embeddings and foundation-model techniques, measures business impact, and partners with business, product, engineering, and analytics teams. Mentors data scientists and AI engineers, influences technical direction, presents findings to leadership, and contributes to hiring and capability development.

The Data Scientist Intern will support Network program execution through data modeling, transformation, analysis, visualization, reporting, and process tooling. Responsibilities include building operational insights and business narratives, improving program observability and accountability, supporting compliance and auditability, collaborating with cross-functional stakeholders, and contributing to special projects. The intern will also participate in mentorship, training, and professional development activities.

Lead design, development, deployment, and monitoring of scalable ML and analytics solutions. Build data pipelines, validate and improve predictive models, translate findings for stakeholders, research emerging AI technologies, and establish governance and best practices to drive product optimization and business value.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps automation and workflows, manages model reliability and incidents, monitors performance and data drift, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical standards, and deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, ensure scalability and reliability, monitor data drift and performance, resolve incidents, and conduct root-cause analysis. Collaborate with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical AI requirements, and connect AI deployments to business outcomes.