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Building machine learning solutions for Peru's largest fintech and banking institutions

The Challenge

Working as a Machine Learning Engineering intern at Yape (Peru's largest fintech with 20M+ users) and Banco de Credito del Peru, I was tasked with developing scalable ML solutions to enhance financial services and improve user experiences across millions of transactions.

Technologies & Tools

Databricks

Utilized Databricks for large-scale data processing, model training, and collaborative development in a unified analytics platform.

Python

Developed machine learning models and data pipelines using Python, leveraging libraries like pandas, scikit-learn, and TensorFlow.

SQL

Executed complex queries for data extraction, transformation, and analysis across massive financial datasets.

Impact & Results

  • •Contributed to ML solutions serving 20+ million users across Peru's digital financial ecosystem
  • •Developed data processing pipelines handling millions of financial transactions
  • •Collaborated with cross-functional teams to deploy ML models in production environments
  • •Gained hands-on experience with enterprise-scale fintech infrastructure and security requirements

Key Learnings

This internship provided invaluable experience in applying machine learning at scale within the fintech industry. I learned how to navigate complex regulatory environments, work with sensitive financial data, and build robust ML systems that can handle the demands of millions of users. The experience reinforced my passion for using technology to democratize financial services and create positive impact in emerging markets.