Cientista de Dados (Databricks)

Accountabilities:: Perform advanced statistical analyses on large volumes of financial data to identify patterns, trends, relationships, and actionable insights. Design, develop, validate, and implement machine learning models for prediction, classification, optimization, and other financial-sector use cases, including risk, credit, fraud, churn, and pricing. Manage the end-to-end lifecycle of analytical and machine learning models, including experimentation, versioning, deployment, monitoring, and continuous improvement. Develop, train, and operationalize models and data pipelines using Databricks. Write efficient Python, SQL, and PySpark code for data extraction, transformation, cleaning, processing, and machine learning implementation at scale. Apply cloud platforms and distributed data technologies to store, process, and analyze large datasets. Collaborate with multidisciplinary teams across business, data engineering, risk, and product to understand requirements and design data-driven solutions. Present analytical findings and model results clearly to both technical and non-technical stakeholders. Support the development of junior data scientists through knowledge sharing, mentoring, and technical guidance. Contribute to model governance and MLOps practices, including model monitoring, drift detection, and CI/CD where applicable. Requirements Proven professional experience as a Senior Data Scientist or in a comparable senior-level data science position. Practical experience delivering data science projects within the financial sector, including banking, fintech, insurance, payments, investments, or similar environments. Hands-on experience with Databricks in professional data science or machine learning projects. Experience with MLflow or equivalent tools for experiment tracking and model lifecycle management. Advanced SQL skills and practical experience with Spark and PySpark. Experience with data modeling and working with large-scale datasets. Strong knowledge of statistics, statistical analysis, and quantitative modeling techniques. Professional proficiency in Python. Experience with machine learning frameworks such as scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Practical experience with cloud platforms for data storage, processing, analytics, or machine learning. Strong communication and collaboration skills, with the ability to work effectively across multidisciplinary teams. Familiarity with regulated models, including model validation, explainability, and governance, is a plus. Knowledge of MLOps practices such as model CI/CD and drift monitoring is desirable. Benefits Fully remote work in Brazil. CLT employment, 40 hours per week. Flexible working hours. SulAmérica health and dental insurance, extended to dependents. Flexible Caju Benefits card for food and meal allowances. Life insurance. Home-office allowance through a flexible Caju Benefits card. Wellhub (formerly Gympass). Sesc membership extended to dependents, with access to services throughout Brazil. TotalPass. Corporate learning platform with more than 20,000 courses for personal and professional development. Language Academy for language learning. Internal talks and technical chapters for sharing practices, studies, projects, and knowledge with colleagues who have similar skills. Pet health plan through Guapeco. OnHappy travel benefit providing flexibility for leisure travel. BYOD option with a monthly payment for using your personal notebook, while company equipment is also available. Birthday celebration benefit, including a cake. Referral rewards program for successful employee recommendations.