Top Tech Jobs & Startup Jobs in Dublin

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 embeddings and foundation models, measures business impact, partners with cross-functional stakeholders, presents technical recommendations, and mentors data scientists and AI engineers. Contributes to hiring, capability development, and the long-term technical direction of the AI organization.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive models, recommendations, forecasting, fraud detection, and customer analytics. Defines experimentation frameworks, applies foundation-model embeddings, measures business impact, partners across functions, presents technical recommendations, and mentors data scientists and AI engineers. The role also contributes to hiring, capability development, and the AI organization’s technical direction.

Leads the design, development, deployment, and evaluation of machine learning solutions for forecasting, recommendations, propensity modeling, fraud detection, and customer analytics. Defines modeling and experimentation strategies, applies foundation-model embeddings, measures business impact, partners with cross-functional stakeholders, and mentors data scientists and AI engineers. The role also contributes to technical direction, hiring, and capability development.

Leads architecture, design, development, modernization, and delivery of scalable cloud-native experimentation and analytics software. Builds services, APIs, data pipelines, and analytical capabilities using AWS, Azure, Databricks, Spark, and PySpark. Provides technical leadership across distributed teams, drives design and code reviews, mentors engineers, and champions security, reliability, observability, performance, and continuous delivery.

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.

Lead architecture and hands-on development of Mastercard’s cloud-native experimentation and analytics platform. Design scalable services, APIs, data pipelines, and distributed systems using AWS, Azure, Databricks, and Spark/PySpark. Drive modernization, technical strategy, design reviews, delivery across global teams, and engineering best practices for security, reliability, observability, and performance. Mentor engineers and collaborate with product, data science, QA, security, and operations teams.

Leads architecture, design, development, and delivery of scalable cloud-native experimentation and analytics platforms. Builds distributed services, APIs, data pipelines, and analytical capabilities using AWS, Azure, Databricks, Spark, and modern software practices. Drives modernization, technical strategy, architecture reviews, cross-team execution, reliability, security, observability, and continuous delivery while mentoring engineers and coordinating globally distributed teams.

Lead the architecture, design, development, and modernization of Mastercard’s cloud-native experimentation and analytics platform. Build scalable services, APIs, data pipelines, and analytical capabilities using AWS, Azure, Databricks, and Spark/PySpark. Drive technical strategy, system design, code reviews, architecture decisions, and cross-team execution while mentoring engineers and ensuring secure, reliable, observable, high-performance software delivery.

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 architecture, design, coding, testing, and delivery of a cloud-native experimentation and analytics platform. Builds scalable services, APIs, data pipelines, and distributed systems using AWS, Azure, Databricks, Spark, and Kubernetes. Drives modernization, technical strategy, architecture reviews, reliability, security, observability, and continuous delivery. Coordinates globally distributed teams, partners cross-functionally, mentors engineers, and ensures production-quality solutions aligned with enterprise standards.