Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive models, recommendation systems, forecasting, fraud detection, and customer analytics. Defines experimentation frameworks, applies foundation-model embeddings, measures business impact, 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 long-term technical direction of the AI organization.
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Lead Data Scientist, AI Engineering
Lead Data Scientist, AI Engineering
Overview
Mastercard's AI Centre of Excellence is building the next generation of AI capabilities powered by large-scale transaction data, machine learning, and foundation models. We are transforming how AI solutions are developed by enabling teams to leverage reusable learned intelligence rather than building bespoke feature-engineering pipelines for every use case.
We are seeking a Lead Data Scientist, AI Engineering to lead the development of advanced machine learning solutions across domains. This role combines deep expertise in predictive modelling, experimentation, and technical leadership to deliver measurable business impact.
What You'll Work On
This role focuses on applying machine learning, predictive modelling, and foundation-model representations to solve business problems at scale. Typical use cases include forecasting, propensity modelling, recommendation systems, behavioural analytics, and customer intelligence.
While familiarity with Generative AI is beneficial, this is primarily an applied machine learning and data science leadership role rather than a conversational AI, RAG, or agentic systems engineering position.
Role / Key Responsibilities
Lead the design, development, and deployment of machine learning solutions that solve high-impact business problems.
Define modelling approaches, experimentation frameworks, and success metrics for AI initiatives.
Apply foundation-model embeddings and modern machine learning techniques to improve model performance and accelerate development.
Drive projects from problem definition through model deployment and business impact measurement.
Establish robust evaluation frameworks and benchmark new approaches against existing solutions.
Partner with business, product, engineering, and analytics teams to identify and prioritise opportunities.
Present technical findings and recommendations to stakeholders and senior leadership.
Mentor and develop data scientists and AI engineers through technical guidance, reviews, and coaching.
Contribute to hiring, capability development, and the long-term technical direction of the AI organisation.
All About You
Required Experience
Proven experience leading machine learning projects from concept through production deployment.
Experience solving predictive modelling problems such as attrition, forecasting, recommendation systems, propensity modelling, fraud detection, risk modelling, or customer analytics.
Strong track record of delivering measurable business outcomes through machine learning.
Experience leading technical teams, mentoring practitioners, and influencing technical direction.
Required Technical Skills
Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation.
Advanced Python and SQL skills.
Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.
Experience with feature engineering, representation learning, embeddings, and downstream machine learning workflows.
Familiarity with transformer-based models and foundation-model applications.
Experience working with Databricks, Spark, Azure, AWS, or GCP.
Leadership & Communication
Strong problem-solving and decision-making skills.
Ability to lead through influence across cross-functional teams.
Excellent communication and stakeholder management capabilities.
Ability to translate complex technical concepts into actionable business insights.
Minimum Qualifications
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
8+ years of experience in machine learning, data science, AI, or advanced analytics.
Experience developing and deploying machine learning models in production environments.
Experience leading technical projects or teams.
Preferred Qualifications
Master's degree or PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Mathematics, or a related field.
Experience with foundation models, embeddings, or representation learning.
Experience in financial services, payments, banking, fintech, fraud, marketing analytics, or customer intelligence.
Publications, patents, conference presentations, or other evidence of technical thought leadership.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Lead Data Scientist, AI Engineering
Lead Data Scientist, AI Engineering
Overview
Mastercard's AI Centre of Excellence is building the next generation of AI capabilities powered by large-scale transaction data, machine learning, and foundation models. We are transforming how AI solutions are developed by enabling teams to leverage reusable learned intelligence rather than building bespoke feature-engineering pipelines for every use case.
We are seeking a Lead Data Scientist, AI Engineering to lead the development of advanced machine learning solutions across domains. This role combines deep expertise in predictive modelling, experimentation, and technical leadership to deliver measurable business impact.
What You'll Work On
This role focuses on applying machine learning, predictive modelling, and foundation-model representations to solve business problems at scale. Typical use cases include forecasting, propensity modelling, recommendation systems, behavioural analytics, and customer intelligence.
While familiarity with Generative AI is beneficial, this is primarily an applied machine learning and data science leadership role rather than a conversational AI, RAG, or agentic systems engineering position.
Role / Key Responsibilities
Lead the design, development, and deployment of machine learning solutions that solve high-impact business problems.
Define modelling approaches, experimentation frameworks, and success metrics for AI initiatives.
Apply foundation-model embeddings and modern machine learning techniques to improve model performance and accelerate development.
Drive projects from problem definition through model deployment and business impact measurement.
Establish robust evaluation frameworks and benchmark new approaches against existing solutions.
Partner with business, product, engineering, and analytics teams to identify and prioritise opportunities.
Present technical findings and recommendations to stakeholders and senior leadership.
Mentor and develop data scientists and AI engineers through technical guidance, reviews, and coaching.
Contribute to hiring, capability development, and the long-term technical direction of the AI organisation.
All About You
Required Experience
Proven experience leading machine learning projects from concept through production deployment.
Experience solving predictive modelling problems such as attrition, forecasting, recommendation systems, propensity modelling, fraud detection, risk modelling, or customer analytics.
Strong track record of delivering measurable business outcomes through machine learning.
Experience leading technical teams, mentoring practitioners, and influencing technical direction.
Required Technical Skills
Strong expertise in machine learning, predictive analytics, statistical modelling, and experimentation.
Advanced Python and SQL skills.
Experience with machine learning frameworks such as Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
Strong understanding of classification, regression, forecasting, recommendation systems, ranking, clustering, and anomaly detection.
Experience with feature engineering, representation learning, embeddings, and downstream machine learning workflows.
Familiarity with transformer-based models and foundation-model applications.
Experience working with Databricks, Spark, Azure, AWS, or GCP.
Leadership & Communication
Strong problem-solving and decision-making skills.
Ability to lead through influence across cross-functional teams.
Excellent communication and stakeholder management capabilities.
Ability to translate complex technical concepts into actionable business insights.
Minimum Qualifications
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
8+ years of experience in machine learning, data science, AI, or advanced analytics.
Experience developing and deploying machine learning models in production environments.
Experience leading technical projects or teams.
Preferred Qualifications
Master's degree or PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Mathematics, or a related field.
Experience with foundation models, embeddings, or representation learning.
Experience in financial services, payments, banking, fintech, fraud, marketing analytics, or customer intelligence.
Publications, patents, conference presentations, or other evidence of technical thought leadership.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
- Abide by Mastercard's security policies and practices;
- Ensure the confidentiality and integrity of the information being accessed;
- Report any suspected information security violation or breach, and
- Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.
Mastercard Dublin, Dublin, IRL Office
One South County, South County Business Park, Dublin, Dublin, Ireland, D18
Similar Jobs at Mastercard
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
Leads the design, development, deployment, and evaluation of machine learning solutions, including forecasting, recommendation, propensity, fraud, and customer analytics models. Defines experimentation frameworks, applies foundation-model embeddings, measures business impact, and partners with product, engineering, analytics, and business teams. Mentors data scientists and AI engineers, presents technical recommendations to leadership, supports hiring, and shapes the organization’s long-term technical direction.
Top Skills:
SparkAWSAzureDatabricksEmbeddingsFoundation ModelsGCPLightgbmMachine LearningPredictive AnalyticsPythonPyTorchRepresentation LearningScikit-LearnSQLTensorFlowTransformer ModelsXgboost
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
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 and success metrics, measures business impact, partners with cross-functional stakeholders, presents findings to leadership, and mentors data scientists and AI engineers. The role also contributes to hiring, capability development, and the technical direction of the AI organization.
Top Skills:
SparkAWSAzureDatabricksFoundation ModelsGoogle Cloud PlatformLightgbmPythonPyTorchScikit-LearnSQLTensorFlowTransformer ModelsXgboost
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
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 representations, measures business impact, and partners with cross-functional stakeholders. Mentors data scientists and AI engineers, presents recommendations to leadership, contributes to hiring, and helps shape the organization’s technical direction.
Top Skills:
SparkAWSAzureDatabricksEmbeddingsFoundation ModelsGCPLightgbmPythonPyTorchRepresentation LearningScikit-LearnSQLTensorFlowTransformer ModelsXgboost
What you need to know about the Dublin Tech Scene
From Bono and Oscar Wilde to today's tech leaders, Dublin has always attracted trailblazers, with more than 70,000 people working in the city's expanding digital sector. Continuing its legacy of drawing pioneers, the city is advancing rapidly. Ireland is now ranked as one of the top tech clusters in the region and the number one destination for digital companies, with the highest hiring intention of any region across all sectors.
