Cientista de Dados (Databricks)

As a Cientista de Dados (Databricks), you will develop and evolve analytical and predictive models designed to generate measurable business value from data.
You will work across the full model lifecycle, from data analysis and experimentation through validation, deployment, and production monitoring.
The role focuses on large-scale financial data and includes use cases such as risk, credit, fraud, churn, and pricing.
You will leverage Databricks, Python, SQL, Spark, and cloud technologies to build scalable data and machine learning solutions.
Working closely with business, data engineering, risk, and product teams, you will translate complex challenges into practical, data-driven solutions.
The position also offers an opportunity to contribute to MLOps practices, communicate insights to technical and non-technical audiences, and mentor less experienced data scientists.
This is a fully remote role with a flexible 40-hour-per-week CLT schedule and a strong focus on continuous technical and professional development.

• 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.