Top AI & Machine Learning Jobs

Develops scalable data analytics capabilities for Mastercard’s global product teams. Responsibilities include managing and mining large datasets, performing advanced SQL and quantitative analysis, validating and cleansing data, developing reporting and data products, translating business requirements into specifications, and presenting insights through Tableau, Power BI, Excel, and PowerPoint. The role also supports modeling, repeatable processes, stakeholder engagement, and data-driven business decisions.

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.

Develops and deploys machine learning and statistical models for financial applications, including transaction classification, temporal analysis, entity resolution, and risk modeling. Responsibilities include analyzing large structured and unstructured datasets, validating and monitoring model performance, creating scalable solutions, communicating findings to business leaders and clients, and applying NLP, deep learning, and data science best practices.

Designs, builds, and operates enterprise AI platforms across public and private clouds. Develops MLOps, model deployment, monitoring, observability, governance, automation, and infrastructure capabilities supporting machine learning and generative AI workloads. Responsibilities include troubleshooting production systems, improving reliability and scalability, collaborating with engineering, security, and governance teams, evaluating emerging AI technologies, contributing to architecture and platform strategy, and mentoring peers.

Supports Mastercard’s AI Ops team by applying data science, machine learning, and AI agent techniques to detect anomalies, predict operational impact, initiate remediation, and create dashboards, reports, and alerts. Interns collaborate with stakeholders to generate insights for application monitoring, troubleshooting, and planning while gaining mentorship and professional development experience.

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.