Data Scientist 2-2

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

Data Scientist 2-2

Job Description:

Who is 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. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.

Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Overview

Finicity, a Mastercard company, is leading the Open Banking Initiative to increase the Financial Health of consumers and businesses. The Data Science and Analytics team is looking for a Senior Data Scientist. The Data Science team works on Intelligent Decisioning; Financial Certainty; Attribute, Feature, and Entity Resolution; Verification Solutions and much more. Join our team to make an impact across all sectors of the economy by consistently innovating and problem-solving. The ideal candidate is passionate about leveraging data to provide high quality customer solutions. Also, the candidate is a strong technical leader who is extremely motivated, intellectually curious, analytical, and possesses an entrepreneurial mindset.
Role
As a Data Scientist II, you will:
- 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.

All about you:
Essential Skills:
- 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.
Programming and tools: Strong Python skills with libraries such as pandas, NumPy, SciPy, and ML frameworks. Competence in code quality, version control, and writing efficient, maintainable pipelines. Strong SQL skills and familiarity data lake systems. Spark or PySpark experience is helpful.
- 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.
Nice to have:

- 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.

Corporate Security Responsibility

Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and therefore, it is expected that the successful candidate for this position 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.

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.