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Tier-1 brand, mid-level data scientist title, metro location, and broad tech requirements increase applicant competition.
Core data science skills transfer well, but payments and pricing domain knowledge increases industry specificity.
Explicit 5+ years and numerous mandatory technologies and domain skills raise selection strictness.
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Design and deliver scalable data science solutions to support Mastercard’s Global Pricing & Interchange strategy and optimize business performance.
Develop predictive models, advanced analytics, and dashboards to inform pricing, interchange, and commercial decisions across regions and products.
Collaborate with cross-functional teams to translate business needs into analytical frameworks and communicate insights to senior leadership.
Minimum 5 years of experience in data science, advanced analytics, quantitative modeling, or related fields.
Strong technical skills in SQL, Python, and experience with big data technologies such as Spark, Hive, Scala, Hadoop/Cloudera; cloud platforms like Azure/AWS, Snowflake, or Databricks.
Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, Finance, or related field required.
Experience developing and deploying machine learning models and data pipelines; proficiency with data visualization tools (Power BI/Tableau).
Experienced in payments, financial services, fintech, pricing, or commercial analytics domains to understand strategic business levers.
Able to manage technical end-to-end delivery of data products and mentor junior team members in an enterprise setting.
Proficient in applying machine learning, predictive analytics, and AI-driven techniques to influence complex commercial strategy and revenue optimization.