Senior Data Scientist

• Develop predictive churn models to identify customers likely to withdraw investments and transfer funds to other financial institutions
• Perform exploratory data analysis, feature engineering, model training, validation, and optimization of Machine Learning models
• Develop customer segmentation models, identifying behavioral patterns and potential reasons for attrition
• Build recommendation engines to support customer win-back and retention strategies
• Develop investment portfolio optimization solutions, considering risk profile, liquidity, concentration, investment horizon, and suitability rules
• Apply model explainability techniques, such as SHAP, ensuring transparency and traceability of predictions
• Implement temporal validation, backtesting, and model performance evaluation processes
• Work with financial data from internal systems, Open Finance, and investment information
• Develop and operationalize models in Azure Databricks using MLflow, Unity Catalog, and Feature Engineering capabilities
• Collaborate on the implementation of batch inference pipelines, model monitoring, and MLOps processes
• Support the integration of analytical models with generative AI solutions to produce business insights
• Prepare technical documentation and support model validation and governance processes with the client

• Strong experience as a Data Scientist, with hands-on experience developing and deploying Machine Learning models
• Proficiency in Python and SQL for data manipulation, analysis, and modeling
• Experience with Data Science libraries such as Pandas, NumPy, Scikit-learn, and XGBoost or similar tools
• Experience with supervised classification models, churn prediction, and customer segmentation
• Knowledge of feature engineering, feature selection, temporal validation, and hyperparameter optimization techniques
• Experience with predictive model evaluation metrics, probability calibration, and explainability techniques
• Hands-on experience with Databricks for developing and executing analytical models
• Knowledge of MLflow for experiment tracking, version control, and model lifecycle management
• Knowledge of mathematical optimization techniques applied to problems with multiple constraints
• Experience developing reproducible models, preparing technical documentation, and following sound governance practices
• Ability to interact with business teams to understand problems, define analytical approaches, and translate results into recommendations
• Analytical, investigative, and problem-solving mindset, with the ability to address complex challenges

• Remote work
• Mental Health Program – online and in-person psychotherapy
• Support for professional certifications and courses
• Partnerships for postgraduate and MBA programs (Esalq/USP)
• Partnerships with language schools
• Partnerships with gyms and wellness apps (Wellhub)
• Internal talks and discussion groups
• Employee referral bonus
• Happy hours
• Gifts and treats on special occasions