DATA SCIENTIST III

About Inter

As pioneers, we transformed the market by launching Brazil’s first digital bank and continue to shape the future with cutting-edge technology. We have evolved into a Global Financial Super App, delivering complete solutions and leading innovation. Here, work has purpose: creating real opportunities, transforming people’s lives, and reshaping the financial market. This is the Inter way of making things happen. If you want to be part of this transformation and leave your mark, your place is here.

About the Role and Mission

You will join one of Inter's most strategic and technically challenging areas: the Model Risk Management team. Here, your work goes beyond building models — you will be the professional responsible for ensuring that the bank's most critical models are robust, reliable, and ready for the real world. You will deep dive into machine learning and statistical methodologies, questioning assumptions, testing limits, and directly contributing to the solidity of one of Brazil's largest digital financial institutions. If you have a passion for modeling and want to evolve into a technical reference role with a vision of risk and governance, this is your opportunity.

Your Day-to-Day Responsibilities:

• Perform independent validations of machine learning and statistical models, including methodology reproduction and critical analysis of results;
• Conduct robustness, stress, and sensitivity analyses on models critical to the bank;
• Issue technical opinions for model approval or rejection before deployment to production;
• Monitor continuous production models, including drift detection, backtesting, and stability analysis;
• Develop and enhance internal model validation frameworks;
• Prepare detailed technical reports and support internal and external audit demands;
• Collaborate with Data Science, Compliance, Business, and Audit teams;
• Contribute to the technical training of the team.

What We Are Looking For

Mandatory:

• Completed higher education, preferably in technology;
• Solid experience in statistical modeling and/or machine learning;
• Knowledge of the model lifecycle, from development to production monitoring;
• Proficiency in Python and/or R with data analysis and modeling libraries;
• SQL and Git;
• Analytical and critical profile with strong attention to detail;
• Good practices in model development.

Desirable:

• Experience with model implementation in a production environment;
• Experience with independent model validation or model risk management (MRM);
• Knowledge of regulatory frameworks for Model Risk Management (GRM Model risk Management MRM);
• Experience with credit, anti-fraud, or financial risk modeling;
• Intermediate English for reading technical and regulatory documentation.