Description

I’m Daniel Pérez Sanz, a data scientist with a solid foundation in mathematics, economics, and machine learning — and hands-on experience delivering results for institutions like Banco Santander and the UK Ministry of Justice.

I work across the full data lifecycle:

• Building and validating models (Python, R, TensorFlow, Scikit-learn)

• Automating analytics pipelines and QA testing (Bash, Docker)

• Developing interpretable, deployable AI (Grad-CAM, CNNs, credit scoring models)

Whether it’s public policy, fintech, or healthcare, I bring analytical clarity, technical rigor, and a focus on practical solutions.

Languages

Spanish
Native or bilingual
English
Native or bilingual
French
Fluent

Workplace preferences

Remote only

Primarily works remotely

Grupo Santander
Data Scientist
BANKING AND INSURANCE
October 2024 - Today (1 year and 8 months)
Banco Santander, Santander, CB, Spain
Working at Data Analytics & Models department through Quental as part of the testing and quality assurance team, focused on credit risk and regulatory projects such as IFRS 9. Support the development, validation, and testing of critical data processes, models, and AI systems across the bank to ensure accuracy, regulatory compliance, and business alignment.
• Conduct quantitative analysis on credit risk metrics, including default probabilities, morosity, cost of credit, and IFRS 9 provisioning, using Python, R, and SQL.
• Design, test, and optimise risk model pipelines and ETL processes, enhancing the robustness and performance of credit risk systems.
• Develop automated testing frameworks and validation scripts in Python and R to ensure data and model integrity across production workflows.
• Perform statistical testing and backtesting of model outputs against regulatory and internal benchmarks, contributing to risk model governance.
• Lead quality assurance of data assets and analytical tools used for risk estimation and reporting, identifying inconsistencies and improving data reliability.
• Collaborate with cross-functional teams (risk, IT, data science) to support the deployment and monitoring of credit risk models under IFRS 9 guidelines.
• Validate and maintain documentation and audit trails for internal models to ensure transparency and compliance with regulatory standards.
Python R SQL
Ministry of Justice, HM Government
Statistician
October 2021 - August 2022 (10 months)
London, UK
• Headed data migration from SAS to R, improving the weekly work ofthe prison statistics team, resulting in an increase in productivity.
• Implemented process improvements and automation solutions, resulting in 15% increase in productivity.
• Managed 2+ projects per quarter on the prison population ofEngland and Wales, which were awarded 3 times
during the year.
• Produced 15+ comprehensive reports and presentations for the British Parliament summarizing findings and recommendations about prison population, facilitating clear communication, and driving actionable outcomes.
University of York
Newspaper Editor
September 2020 - June 2021 (9 months)
• Headed the science and technology section ofthe newspaper, coordinating a team of 5+ people.
• Accomplished to write 3+ articles per week researching about science.