Jobs at Mastercard

Develop and deploy production machine learning solutions for forecasting, recommendation, propensity modeling, fraud detection, risk modeling, and customer analytics. Define experimentation and evaluation frameworks, apply foundation-model embeddings, measure business impact, and collaborate with product, engineering, analytics, and business teams. Communicate technical findings, establish reusable modeling standards, and mentor junior data scientists.

Leads complex, cross-functional technical programs across engineering teams, regions, architectures, and vendors. Establishes program governance, delivery processes, reporting, and operating rhythms; manages risks and escalations; aligns stakeholders; communicates status to senior leadership; and builds scalable delivery and onboarding models for a growing engineering organization.

Leads complex, cross-functional technical programs from strategy through delivery. Establishes program governance, delivery processes, and operating rhythms; coordinates engineering teams, regions, vendors, and stakeholders; manages escalations, risks, dependencies, and executive communications; and drives predictable execution. The role also supports scaling engineering squads by creating repeatable delivery and onboarding models in an ambiguous, fast-growing environment.

Develop and deploy production machine learning solutions for forecasting, recommendation, propensity modeling, fraud detection, risk modeling, and customer analytics. Define experimentation methods, evaluation frameworks, and success metrics; apply embeddings and foundation-model techniques; and measure business impact. Partner with product, engineering, analytics, and business teams from problem definition through deployment. Communicate insights to technical and nontechnical stakeholders, establish reusable modeling standards, and mentor junior data scientists.

Develop and deploy production machine learning solutions, including forecasting, recommendation systems, propensity modeling, fraud detection, and customer analytics. Define experiments, evaluation frameworks, and success metrics; apply embeddings and foundation-model techniques; and measure business impact. Collaborate with product, engineering, analytics, and business teams, communicate technical findings, establish reusable modeling standards, and mentor junior data scientists.

Develop and deploy production machine learning solutions for forecasting, recommendations, propensity modeling, fraud detection, risk modeling, and customer analytics. Define modeling and experimentation strategies, apply foundation-model embeddings, create evaluation frameworks, and measure business impact. Partner with product, engineering, analytics, and business teams from problem definition through deployment. Communicate findings to technical and nontechnical stakeholders, establish reusable modeling standards, and mentor junior data scientists.

Develop and deploy production machine learning solutions, including forecasting, recommendation, propensity, fraud, and customer analytics models. Define experimentation methods, evaluation frameworks, and success metrics; apply embeddings and foundation-model techniques; and measure business impact. Partner with product, engineering, analytics, and business teams to deliver AI initiatives from concept through deployment. Establish reusable modeling practices, communicate technical insights, and mentor junior data scientists.

Leads complex, cross-functional technical programs across engineering teams, regions, vendors, and organizational domains. Establishes program governance, delivery processes, operating rhythms, and scalable team models. Manages risks, escalations, dependencies, stakeholder alignment, and executive communications while driving predictable delivery and measurable outcomes in an ambiguous, fast-evolving environment.

Develop and deploy production machine learning solutions for forecasting, recommendations, propensity modeling, fraud detection, risk modeling, and customer analytics. Define experiments, evaluation frameworks, and success metrics; apply embeddings and foundation-model techniques; and measure business impact. Partner with product, engineering, analytics, and business teams to deliver AI initiatives. Establish reusable modeling practices, communicate technical insights, and mentor junior data scientists.

Develop and deploy production machine learning solutions for forecasting, recommendations, propensity modeling, fraud detection, risk modeling, and customer analytics. Define modeling and experimentation strategies, build evaluation frameworks, apply foundation-model embeddings, and measure business impact. Partner with product, engineering, business, and analytics teams to deliver AI initiatives, communicate technical findings, establish reusable modeling standards, and mentor junior data scientists.

Leads complex, cross-functional technical programs across engineering teams, regions, vendors, and organizational domains. Establishes program governance, delivery processes, operating rhythms, and scalable team onboarding models. Manages risks, escalations, dependencies, stakeholder alignment, reporting, and executive communications while driving predictable delivery of strategic technical solutions in an ambiguous, fast-evolving environment.

Lead complex, cross-functional technical programs across engineering teams, regions, vendors, and organizational domains. Establish program governance, delivery processes, operating rhythms, and scalable team onboarding models. Manage escalations, risks, dependencies, stakeholder alignment, reporting, and executive communications while driving predictable delivery of strategic technology initiatives in an ambiguous, fast-evolving environment.

Develop and deploy production machine learning solutions for forecasting, recommendations, propensity modeling, fraud detection, risk modeling, and customer analytics. Define experimentation and evaluation frameworks, apply embeddings and foundation models, measure business impact, and partner with product, engineering, analytics, and business teams. The role also contributes to reusable AI standards and mentors junior data scientists.

Leads complex, cross-functional technical programs across engineering teams, regions, architectures, and external vendors. Establishes program governance, delivery processes, operating rhythms, reporting, and scalable collaboration models. Manages risks, escalations, stakeholder alignment, and executive communications while driving predictable delivery and supporting the growth of distributed engineering squads.

Lead complex, cross-functional technical programs from strategy through delivery. Establish program governance, delivery processes, and operating rhythms; coordinate engineering teams, regions, vendors, and stakeholders; manage escalations, risks, dependencies, and executive communications; and improve scalable delivery models for a growing global engineering organization. The role requires independent execution in ambiguous environments, strong agile and scrum expertise, and the ability to align teams toward predictable outcomes.

Lead complex, cross-functional technical programs across engineering teams, regions, vendors, and organizational domains. Establish program governance, delivery processes, operating rhythms, and scalable collaboration models. Manage risks, escalations, dependencies, reporting, and stakeholder alignment while communicating status and decisions to senior leadership. Support the scaling and onboarding of engineering squads and drive predictable delivery in ambiguous, evolving environments.

Develop and deploy production machine learning solutions for forecasting, recommendation, propensity modeling, fraud detection, risk modeling, and customer analytics. Define experimentation methods, evaluation frameworks, and success metrics; apply foundation-model embeddings and modern ML techniques; measure business impact; and collaborate with product, engineering, analytics, and business teams. Communicate technical findings, establish modeling standards, create reusable assets, and mentor junior data scientists.

Leads complex, cross-functional technical programs across engineering teams, regions, vendors, and organizational domains. Establishes program governance, delivery processes, operating rhythms, and scalable collaboration models. Manages risks, escalations, stakeholder alignment, reporting, and accountability while communicating program status and decisions to senior leadership. Supports the growth of engineering squads and drives predictable delivery in ambiguous, fast-evolving environments.

Develop and deploy production machine learning solutions for forecasting, recommendation, propensity modeling, fraud detection, risk modeling, and customer analytics. Define modeling and experimentation strategies, apply foundation-model embeddings, create evaluation frameworks, measure business impact, and collaborate with product, engineering, analytics, and business teams. The role also involves communicating technical insights, establishing modeling standards, developing reusable assets, and mentoring junior data scientists.

Leads complex, cross-functional technical programs across engineering teams, regions, vendors, and organizational stakeholders. Establishes program governance, delivery processes, operating rhythms, and scalable team onboarding models. Manages escalations, risks, dependencies, reporting, and stakeholder communication while driving accountability and predictable delivery. Operates independently in ambiguous, fast-evolving environments and supports strategic execution for a global data, analytics, and technology organization.