Data Scientist 2-1

Data Scientist 2-1

Job Description:

• Work with large, complex datasets to uncover insights and build predictive and decisioning models.


• Apply statistical and analytical techniques (e.g., regression/classification, clustering/segmentation) to create actionable insights and robust model baselines.


• Develop and implement machine learning and (where appropriate) deep learning approaches to solve applied business problems.


• Design, build, and maintain models for financial applications such as transaction classification, temporal analysis, and risk modeling using structured and unstructured data.


• Measure, validate, monitor, and improve model performance over time; define success metrics and implement feedback loops.


• Use best practices to build scalable solutions (reproducibility, testing, documentation, versioning, monitoring) in partnership with engineering.


• Communicate technical problems, trade-offs, and results succinctly to internal stakeholders and, as needed, to clients.


• Propose practical, creative solutions to challenges that may be new to the team, the organization, or the industry.


• Identify gaps in tooling/process/resources and recommend sustainable improvements that strengthen team delivery.

• 3–5 years of experience in data science / machine learning, including building models end-to-end (problem framing → development → validation → deployment → monitoring).


• Strong foundation in statistical modeling and applied machine learning, with the ability to select appropriate techniques for the problem.


• Machine learning and NLP: Practical experience with supervised and unsupervised learning, neural networks, and NLP techniques. Hands on work with models such as classifiers, predictors, entity extraction, and language models. Experience with LLMs and generative AI tools, including prompt engineering.

• Strong writing and communication skills; can explain analysis and recommendations clearly to varied stakeholders.


• Demonstrated ability to work collaboratively across product, engineering, and analytics partners.

• Experience with financial transactional data (structured + unstructured), transaction classification, risk evaluation, and/or credit risk modeling.


• Experience with productionization patterns and tooling such as containers (Docker), orchestration (Kubernetes), REST APIs, event streams, or similar delivery mechanisms.


• Familiarity with common ML frameworks and libraries and strong practices for experimentation, governance, and monitoring.


• Finance / FinTech domain exposure.

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.