Our Purpose

We work 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. We cultivate a culture of inclusion (https://www.mastercard.us/en-us/vision/who-we-are/diversity-inclusion.html) for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.

Title and Summary

Senior Data Scientist

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.

Our Team:

As consumer preference for digital payments continues to grow, ensuring a seamless and secure consumer experience is top of mind. Optimization Solutions team focuses on tracking of digital performance across all products and regions, understanding the factors influencing performance and the broader industry landscape. This includes delivering data-driven insights and business recommendations, engaging directly with key external stakeholders on implementing optimization solutions (new and existing), and partnering across the organization to drive alignment and ensure action is taken.

• Are you excited about Data Assets and the value they bring to an organization?

• Are you an evangelist for data-driven decision-making?

• Are you motivated to be part of a team that builds large-scale Analytical Capabilities supporting end users across 6 continents?

• Do you want to be the go-to resource for data science & analytics in the company?

The Role:

• Work closely with global optimization solutions team to architect, develop, and maintain advanced reporting and data visualization capabilities on large volumes of data to support data insights and analytical needs across products, markets, and services

• The candidate will be responsible for developing solutions using Machine Learning and Deep Learning models, such as LSTM, and generating actionable insights to enhance product optimization and support sales enablement.

• Prototype new algorithms, experiment, evaluate and deliver actionable insights.

• Drive the evolution of products with an impact focused on data science and engineering.

• Designing and deploying machine learning models in production environments including deep learning models such as LLMs and self-running artificial intelligence (AI) software to automate predictive models, ensuring they leverage GPU acceleration for optimal performance.

• Implement data engineering best practices to support the end-to-end machine learning lifecycle.

• Perform data ingestion, aggregation, and processing on high volume and high dimensionality data to drive and enable data unification and produce relevant insights.

• Continuously innovate and determine new approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations.

• Apply knowledge of metrics, measurements, and benchmarking to complex and demanding solutions.

All about You

• A superior academic record at a leading university in Computer Science, Data Science, Technology, mathematics, statistics, or a related field or equivalent work experience

• Minimum 5 years of experience in data management, data mining, data analytics, data reporting, data product development and quantitative analysis

• Strong analytical skills with track record of translating data into compelling insights

• Prior experience working in a product development role.

• Knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.

• Proven experience with deep learning frameworks such as TensorFlow, PyTorch, and Keras, and extensive experience in building and deploying LLMs and other neural networks.

• Extensive experience with GPU acceleration for deep learning model training and inference, along with a strong familiarity with CI/CD pipelines and MLOps practices.

• Proficiency in using Python/Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), and SQL to build Big Data products & platforms

• Experience with Enterprise Business Intelligence Platform/Data platform i.e. Tableau, PowerBI is a plus.

• Demonstrated success interacting with stakeholders to understand technical needs and ensuring analyses and solutions meet their needs effectively.

• Ability to build a strong narrative on the business value of products and actively participate in sales enablement efforts.

• Able to work in a fast-paced, deadline-driven environment as part of a team and as an individual contributor.

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.