MastercardData Scientist II, Product Data & Analytics
AI in this role
Data Scientist II building internal analytics partnerships, scalable data solutions, and reporting capabilities.
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 II, Product Data & AnalyticsData Scientist II, Product Data & AnalyticsOur Vision
The Product Data & Analytics team builds internal analytics partnerships that strengthen focus on business health, portfolio performance, revenue optimization, initiative tracking, new product development, and go-to-market strategies. We are a hands-on global team that delivers scalable, end-to-end data solutions in close partnership with the business. Through data-driven insights, we influence decisions across Mastercard. Our team includes analytics engineers, data architects, BI developers, data analysts, and data scientists who collectively manage our data assets and analytical solutions.
What We Look For
• Are you excited by the value that data assets can bring to an organization?
• Are you an advocate for data-driven decision-making?
• Are you motivated to be part of a global analytics team that builds large-scale analytical capabilities for users across continents?
• Are you interested in proactively improving data-driven decisions for a global corporation?
Role Responsibilities
• Partner with global and regional teams to design, develop, and maintain advanced reporting and data visualization capabilities using large-scale data sets across products, markets, and services.
• Translate business requirements into clear solution specifications and deliver high-quality outputs on time.
• Create repeatable processes for ETL development, data modelling, and reporting.
• Use analytical tools to manipulate large-scale databases, synthesize insights, and present findings through Tableau, Power BI, Excel, and PowerPoint.
• Apply quality control, data validation, and cleansing processes to both new and existing data sources.
• Bring passion, curiosity, and technical expertise to every engagement.
Candidate Profile
• 3–5 years of experience in data engineering and management, data mining, data analytics, reporting, data product development, or quantitative analysis.
• Advanced SQL skills, including the ability to write optimized queries for large data sets.
• Experience writing stored procedures, functions, and views is preferred.
• Experience with platforms and environments such as Databricks, Cloudera Hadoop, big data technology stacks, SQL Server, Microsoft BI Stack, cloud platforms, Snowflake, and related technologies.
• Exposure to Python, Scala, Hive, Impala, Spark, cloud technologies, and related tools.
• Experience creating data pipelines using tools such as Alteryx, SSIS, or similar platforms.
• Experience with at least one data visualization tool, such as Power BI, Tableau, or a similar platform.
• Exposure to AI, machine learning, or agent creation is a plus.
• Advanced knowledge of job scheduling tools such as Apache Airflow, NiFi, or similar workflow orchestration tools.
• Experience applying data validation, quality control, and cleansing processes to new and existing data sources.
• Ability to present data findings in a clear, readable, and insight-driven format, including support decks for stakeholders.
• Ability to engage with management and internal stakeholders to gather and clarify requirements.
• Ability to work effectively in a team, apply sound judgment, and operate through ambiguity.
• Experience in financial institutions or payments is a plus.
Education
• Bachelor’s or master’s degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics, or a related field; M.S. or M.B.A. preferred.
Additional Competencies
• Excellent English language, quantitative, technical, and communication skills, both written and verbal.
• Strong analytical and problem-solving skills.
• High attention to detail and commitment to quality.
• Creativity and innovation in approaching analytical challenges.
• Self-motivated working style with a strong sense of urgency.
• Project management discipline with an ability to identify and mitigate risks.
• Ability to prioritize and manage multiple tasks simultaneously.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks carry inherent risk. Everyone working for, or on behalf of, Mastercard is responsible for protecting information security and is expected to:
• Abide by Mastercard’s security policies and practices.
• Ensure the confidentiality and integrity of all information accessed.
• Report any suspected information security violation or breach.
• Complete all required periodic security training in accordance with Mastercard 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.
How we rate this
Data Scientist II, Product Data & Analytics at Mastercard rates 10 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where data science was part of your work. What did you do?
- Tell me about a project where data analytics was part of your work. What did you do?
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where data modeling was part of your work. What did you do?
- Tell me about a project where data visualization was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Data Science, Data Analytics, ETL, Data Modeling, and Data Visualization. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
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