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Tier-1 brand, metro location, and broad generalist data skill requirements increase applicant competition.
Core data engineering and analytics skills are transferable, but banking risk and regulatory expertise require specialization.
Mandatory technical skills (SQL, Python, SAS) and banking risk/regulatory expectations raise screening rigor.
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Own and enhance data sourcing, transformation, mapping, and automation processes for risk data to support analytics, reporting, and visualization.
Collaborate with cross-functional risk teams and stakeholders to develop data products, validate solutions, and embed BAU controls in risk data governance.
Lead migration of proof-of-concept projects into production and ensure adherence to data quality and regulatory standards including BCBS 239.
Bachelor's or Master's degree in a technical field (statistics, mathematics, computer science, etc.) mandatory.
Extensive programming experience in SQL, Python, SAS, and Excel automation.
Experience working with data for model development and data management best practices essential.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in banking risk management domains such as Stress Testing, IFRS9, or credit risk modeling preferred.
Strong analytical and problem-solving skills combined with hands-on technical coding and data science capabilities (Python, SQL, Tableau).
Able to operate in cross-functional teams and communicate effectively across senior management and technical stakeholders.