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BBVA
BBVA
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BBVA is a global banking and financial services group working to modernize how people and businesses manage money. Its activities span corporate and investment banking, fintech, and digital banking, serving 71.5 million customers through technology-enabled financial solutions. With more than 121,000 employees, BBVA brings together expertise across banking, consulting, and marketing in a collaborative environment focused on innovation, digital transformation, and professional excellence.
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Credit Risk Data Analyst – BBVA, Mexico City Hybrid

Join BBVA as a credit risk data analyst supporting retail lending portfolios with Python, PySpark, SAS, and SQL. Build KPIs, validate data, and apply IFRS 9 principles to credit risk analysis.

MXMexicoHybridFull-timeMiddleSeniorEnglish RequiredSpanish RequiredData Analyst
ETLPySparkPythonSQL

Description

  • Analyze and optimize end-to-end processes across retail credit portfolios, including cards, consumer loans, auto loans, and mortgages.
  • Manage, transform, and integrate data from legacy platforms and newer tools through ETL workflows using Python, PySpark, SAS Guide, and SQL.
  • Create and automate collections and performance reports, including key performance indicators.
  • Structure and analyze databases to identify customer behavior patterns and evaluate strategy effectiveness.
  • Run critical monthly processes, including day-zero activities, quality testing, and database consistency checks.
  • Apply IFRS 9 requirements to provisions, expected losses, exposure at default, credit risk, and operational risk.
  • Work with multidisciplinary teams across Risk, Finance, and Planning.
  • Prepare executive presentations covering findings and proposed actions.
  • Handle large data volumes in Big Data environments while managing tight deadlines and monthly operational peaks.
  • Convert complex analyses into clear, actionable recommendations.

Requirements

  • Automated data analysis using Python for Data Analysis and SQL Fundamentals.
  • Experience with PySpark, SAS Guide, and ETL workflows.
  • Advanced Excel skills, including pivot tables and macros, plus proficiency with Google Workspace.
  • Knowledge of credit risk, collections, or IFRS 9 requirements covering expected losses, exposure at default, and provisions.
  • Experience using artificial intelligence tools such as ChatGPT and Gemini.
  • Intermediate English for reading and understanding technical documentation.
  • Knowledge of payment behavior prediction models.
  • Experience with Power BI.
  • SAS Certified Specialist certifications.
  • Coursework or postgraduate study in Data Science, Machine Learning, or Big Data.
  • Bachelor’s degree or engineering qualification in Actuarial Science, Economics, Mathematics, Finance, Business Administration, or a related field; current students and graduates may apply.
  • Three to five years of experience analyzing financial data, credit portfolios, risk, or collections.
  • Analytical and structured approach, strong attention to detail, and a data-driven mindset.
  • High stress tolerance, resilience, and self-management.
  • Execution-focused approach with a strong commitment to quality.
  • Leadership grounded in giving and receiving feedback.
  • Active collaboration skills.

Benefits

  • Reasonable adjustments are available throughout the selection process to support full participation.
  • A diverse, inclusive, and equitable workplace.
  • A collaborative and inclusive work environment.

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