Description: It is very important that you have and/or know: * Degree in Statistics, Mathematics, Engineering, Computer Science, Economics, or related fields; * Proven experience in data science applied to credit/finance (preferably in loans), including the use and analysis of data from credit bureaus. Strong expertise in: * Python/PySpark programming; * Advanced SQL for handling large volumes of data; * Supervised and unsupervised machine learning techniques; * Experience with cloud platforms (GCP, AWS, or Azure) and MLOps; * Knowledge of version control tools (Git) and data pipelines (Airflow, dbt, etc). The role’s challenges are: * Predictive and Statistical Modeling: Develop, train, and validate predictive models applied to the loan product lifecycle and customer management, such as propensity and churn; * Data Management and Feature Engineering: Explore, structure, and enrich internal and external databases to generate high-performance variables for the loan context, and ensure data quality, governance, and traceability used in models; * CRM Products and Strategy: Create intelligent segmentation for loan origination and cross-sell campaigns, and support business teams in defining strategies to increase conversion; * Analysis and Experimentation: Conduct exploratory and causal analyses to understand customer behavior drivers, as well as design and evaluate A/B tests for validating loan portfolio offers; * Production and Scalability: Collaborate with data engineers and technology teams to ensure model deployment with monitoring metrics, performance, and fairness; * Consulting Role: Support Product, CRM, Credit, and Risk stakeholders in data-driven customer decision-making. 2512260202491936476