ARC Research Manager – Quantitative Research Consultant, Data Processing (L8)

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 ARC Research Manager – Quantitative Research Consultant, Data Processing (L8) 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 Advisors and Consulting Services. ARC partners closely with consulting teams and internal stakeholders to design, execute, and deliver rigorous research that informs strategic decision making for clients across industries and markets. 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 visualization and communication of quantitative research findings for global projects. Role: As a Quantitative Research Consultant – Data Processing, 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. Candidates for this position should have strong attention to detail, a solid understanding of quantitative data structures, and an interest in developing analytical and statistical skills. Key responsibilities include: • Perform data cleaning, validation, and preparation of survey datasets for analysis • Apply standard data quality checks, logic validations, and consistency rules • Review in‑field and post‑field data to identify issues such as outliers, missing data, or inconsistencies • Create structured outputs such as cross‑tabulations and analysis‑ready datasets • Execute data merging, variable creation, and dataset structuring following defined standards • Run basic analytical outputs (e.g., distributions, simple comparisons, standard metrics) as required • Analyze quantitative survey data using standard techniques to produce structured outputs and summaries • Flag patterns, anomalies, or data concerns to researchers and senior analysts • Maintain clear documentation, codebooks, and processing notes • Work closely with fieldwork, scripting, and visualization teams to ensure smooth project hand‑offs Qualifications & Skills: • 3-5 years’ experience at a research agency or in an in‑house role supporting quantitative research data processing • Bachelor’s degree in Statistics, Mathematics, Economics, Engineering, Computer Science, or a related field, or equivalent practical experience • Hands‑on experience with data processing or statistical tools (e.g., SPSS, Q, R, Excel, DisplayR, or similar) is mandatory • Understanding of quantitative research data structures and processing workflows • Familiarity with basic analytical concepts and outputs (e.g., distributions, cross‑tabs, basic comparisons) • Strong attention to detail and quality orientation • Ability to manage multiple datasets and tasks simultaneously • Comfortable working in fast‑paced, deadline‑driven environments • Experience supporting multi‑market or regional studies is a plus All About You • Detail oriented and focused on delivering high quality, accurate data • Comfortable taking ownership of defined workstreams and delivering against timelines • Confiden ... (truncated, view full listing at source)