Top AI & Machine Learning Jobs

Leads applied machine learning and data science initiatives from problem definition through production deployment and business impact measurement. Develops predictive models, experimentation frameworks, foundation-model embeddings, and evaluation benchmarks for forecasting, recommendation, fraud, risk, and customer analytics use cases. Partners with cross-functional stakeholders, communicates technical insights to leadership, mentors data scientists and AI engineers, and contributes to hiring and long-term technical strategy.

Leads the design, development, deployment, and evaluation of machine learning solutions for business problems such as forecasting, recommendations, fraud detection, and customer analytics. Defines modeling and experimentation strategies, applies foundation-model embeddings, measures business impact, partners with cross-functional stakeholders, presents technical recommendations, mentors data scientists and AI engineers, and contributes to hiring and the organization’s technical direction.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive models, recommendation systems, forecasting, embeddings, and foundation-model applications. Defines experimentation frameworks, metrics, and technical direction while partnering with business, product, engineering, and analytics teams. Presents insights to senior stakeholders, mentors data scientists and AI engineers, and drives projects from problem definition through measurable business impact and production deployment.

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.