As a Lead ML Ops & Data Engineer, you'll develop automated pipelines, collaborate with teams to ensure efficient ML Ops, and maintain high-quality data sources.
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 ML Ops & Data Engineer
Lead ML Ops & Data Engineer - Security Solutions
About Mastercard
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Team Overview
The Security Solutions Data Science team develops and deploys AI/ML models powering Mastercard's authentication and authorization networks, with a focus on fraud and financial crime prevention. We deliver production-ready models and automated, scalable pipelines for Fortune 500 clients in fintech and banking.
Role Overview
As a Lead ML Ops & Data Engineer, you will play a critical role in enabling the Data Science team to operate efficiently and at scale. You will be the primary owner of your workstreams, with support from cross-functional colleagues. You will develop robust tools and pipelines to automate tasks, establish best practices for ML Ops, and curate and maintain high-quality data sources. You will work closely with both the Data Science and Engineering teams to ensure seamless collaboration, anticipate changes, and drive continuous improvement in our ML operations.
Key Responsibilities
- Develop, deploy, and maintain tools and automated pipelines for data science workflows, reducing manual effort and risk of error.
- Acquire expertise on how different environments interact with each other, and maintain documentation to inform the DS team.
- Collaborate with Engineering and DevOps to maintain alignment by anticipating upcoming changes in infrastructure, data sources, or deployment environments, and plan ML Ops work accordingly.
- Establish, document, and promote clear processes and best practices around ML Ops, working closely with both Data Science, Engineering, and DevOps teams.
- Curate, maintain, and document a collection of clean, reliable data sources for the Data Science team.
- Monitor, troubleshoot, and optimize ML pipelines and data workflows.
- Contribute to the evaluation and adoption of new ML Ops tools and technologies.
Essential skills
- Strong experience in ML Ops, DevOps, or Data Engineering roles.
- Capable of writing well-tested, maintainable code to support live and new models.
- Strong project management skills, and a strong determination to progress through constraints.
- Comfortable communicating with a range of stakeholders, including subject matter experts, data scientists, software engineers, and platform architects.
- Prioritise delivering value, in the spirit of "done is better than perfect".
- Proficiency in Python and experience with workflow orchestration tools (e.g., Airflow, MLflow, or similar).
- Experience building and maintaining data pipelines.
- Experience with CI/CD for ML systems.
- Bachelor's degree in Computer Science, Engineering, or a related STEM field.
Preferred skills
- Experience working in financial services, payments, or other regulated industries.
- Experience supporting Data Science teams in a production environment.
- Experience optimising solution performance with a constrained set of technologies.
- Knowledge of monitoring, logging, and alerting for ML systems.
- Loves building tools and processes that make teams more efficient and effective.
- Loves working with error-prone, messy, disparate data.
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 ML Ops & Data Engineer
Lead ML Ops & Data Engineer - Security Solutions
About Mastercard
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Team Overview
The Security Solutions Data Science team develops and deploys AI/ML models powering Mastercard's authentication and authorization networks, with a focus on fraud and financial crime prevention. We deliver production-ready models and automated, scalable pipelines for Fortune 500 clients in fintech and banking.
Role Overview
As a Lead ML Ops & Data Engineer, you will play a critical role in enabling the Data Science team to operate efficiently and at scale. You will be the primary owner of your workstreams, with support from cross-functional colleagues. You will develop robust tools and pipelines to automate tasks, establish best practices for ML Ops, and curate and maintain high-quality data sources. You will work closely with both the Data Science and Engineering teams to ensure seamless collaboration, anticipate changes, and drive continuous improvement in our ML operations.
Key Responsibilities
- Develop, deploy, and maintain tools and automated pipelines for data science workflows, reducing manual effort and risk of error.
- Acquire expertise on how different environments interact with each other, and maintain documentation to inform the DS team.
- Collaborate with Engineering and DevOps to maintain alignment by anticipating upcoming changes in infrastructure, data sources, or deployment environments, and plan ML Ops work accordingly.
- Establish, document, and promote clear processes and best practices around ML Ops, working closely with both Data Science, Engineering, and DevOps teams.
- Curate, maintain, and document a collection of clean, reliable data sources for the Data Science team.
- Monitor, troubleshoot, and optimize ML pipelines and data workflows.
- Contribute to the evaluation and adoption of new ML Ops tools and technologies.
Essential skills
- Strong experience in ML Ops, DevOps, or Data Engineering roles.
- Capable of writing well-tested, maintainable code to support live and new models.
- Strong project management skills, and a strong determination to progress through constraints.
- Comfortable communicating with a range of stakeholders, including subject matter experts, data scientists, software engineers, and platform architects.
- Prioritise delivering value, in the spirit of "done is better than perfect".
- Proficiency in Python and experience with workflow orchestration tools (e.g., Airflow, MLflow, or similar).
- Experience building and maintaining data pipelines.
- Experience with CI/CD for ML systems.
- Bachelor's degree in Computer Science, Engineering, or a related STEM field.
Preferred skills
- Experience working in financial services, payments, or other regulated industries.
- Experience supporting Data Science teams in a production environment.
- Experience optimising solution performance with a constrained set of technologies.
- Knowledge of monitoring, logging, and alerting for ML systems.
- Loves building tools and processes that make teams more efficient and effective.
- Loves working with error-prone, messy, disparate data.
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.
Top Skills
Airflow
Ci/Cd
Data Engineering
Ml Ops
Mlflow
Python
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