Overview
Mastercard is looking for a talented Data Scientist / Machine Learning Engineer to join the Mastercard Foundry AI & ML team in R&D in our Dublin location. In this role you will be a leading a highly agile team building exiting and innovative data-driven AI/ML products delivered at scale to global markets. You will work closely with our Director of Data Science.
Our team is built on a foundation of research and development, mining innovation internally, innovating new product lines with emerging technology, managing new products from inception to market validation and engaging strategically with startups to shape the future of commerce with and for our customers. At Mastercard you will help define the future of commerce globally.
This team will have a diverse focus both in terms of geography and variety of technology challenges driving hard to bring innovative payment solutions to market.
Role
- The role will be based in the AI/ML practice within Mastercard Foundry R&D, focused on developing new product and services using AI & ML
- The successful candidate will be sufficiently flexible to work across a range of projects covering artificial intelligence, machine learning and data science
- The successful candidate will be well-versed in a wide range of data science, machine learning and artificial intelligence techniques, and will be able to make informed judgements as to the most appropriate approach to the problem at hand
- The successful candidate will have a strong mathematics and/or statistical background, and a proven ability to apply theoretical approaches to practical problems
- The candidate will have an appetite to develop new commercial products and solutions employing AI/ML/DS approaches
- The successful candidate will be part of a multi-disciplinary team, and will report to the Lead Data Scientist on assigned projects
- Expertise and fluency in modern AI/ML technologies such as Scikit Learn, TensorFlow, PyTorch, PANDAS, Spark etc.
- Familiarity with turning experimental results (e.g. from Jupyter notebooks) into production quality code
- Familiarity with current deployment technologies, e.g. Kubernetes and cloud infrastructure
- Strong software engineering practices, including source code control, unit testing, etc.
- Track record of working in an agile environment with a multi-disciplinary team
Education
- MSc in Data Science, Artificial Intelligence or Machine Learning (or equivalent) a minimum. PhD in those topics strongly preferred.
- 3-5 years post-graduate experience in commercial environments
