Research Analyst - Primary Market Research, Data Processing

Mastercard — Gurgaon, India

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 Research Analyst - Primary Market Research, Data Processing Job Description: Research Analyst – Primary Market Research, Data Processing Location: Gurgaon, India Team: Advisors Research Center (ARC) – Global Facilities India (GFI) Overview The Advisors Research Center (ARC) is a global team of researchers within Mastercard that delivers high‑quality, end‑to‑end research in support of Mastercard’s Advisory and Consulting Services. As part of ARC’s continued growth, Global Facilities India (GFI) supports global and regional ARC engagements by providing scalable, high‑quality quantitative research delivery. The GFI team works closely with ARC researchers and consultants to ensure consistency, speed, and quality across research execution and outputs. This role sits within the ARC Quant Research capability in GFI and supports the preparation, processing, and quality assurance of quantitative research data for global projects Role: As a Quantitative Research Data Processing Specialist, you will support data processing workstreams across quantitative research projects. You will be responsible for preparing clean, structured, and analysis-ready datasets, applying quality checks, and supporting basic analytical outputs in partnership with senior team members. Key responsibilities: - Perform data cleaning, validation, and preparation of survey datasets for analysis - Review infield and postfield data to identify issues such as outliers, missing data, or

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