About the Company
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. Become Sangue Laranja.
About the Role and Mission
You will be part of our Pricing team and work on data science projects.
Responsibilities
- Develop, validate, and maintain predictive and analytical models that support the bank's strategic decisions;
- Translate business problems into quantitative hypotheses and models;
- Perform exploratory data analysis (EDA) and feature engineering;
- Evaluate model performance, stability, and bias;
- Document models clearly and auditable;
- Support the implementation and continuous monitoring of models in production;
- Present results clearly to technical and executive audiences.
Requirements
Mandatory:
- Complete graduation in Statistics, Computer Science, Economics, or related fields;
- Proactivity, critical vision, and ability to communicate technical results clearly to different levels of audience;
- Solid experience with predictive modeling and applied statistics;
- Proficiency in analytical tools and languages such as Python and SQL;
- Strong skills in data manipulation and model validation;
- Ability for technical documentation and organization;
- Knowledge of version control (git);
- Good programming practices and code organization.
Desirable:
- Previous experience in pricing and/or the financial sector will be a differential;
- Knowledge of Cloud platforms (preferably AWS);
- MLOps concepts;
- Model deployment;
- Automated re-training;
- Containers (Docker) and APIs;
- Experience in financial institutions;
- Experience in pricing projects;
- Previous experience with critical data (risk, credit, liquidity);
- Experience in scaling data solutions from pilot to production;
- Practical knowledge in data governance.