Cientista de Dados Sênior
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLMachine LearningMLFlowData sciencePandasRESTful APIsscikit-learn
Requirements
- Bachelor’s degree (or higher) in Information Technology, Computer Science, Data Science, or related field.
- Proven experience in data science projects, preferably involving fraud detection, anomaly detection, or financial risk analysis.
- Advanced proficiency in Python (Pandas, Scikit-learn, XGBoost, LightGBM) and strong SQL skills.
- Experience working with Microsoft Fabric and Azure Machine Learning (including Synapse, Notebooks, and MLflow).
- Strong experience integrating and consuming REST APIs and working with heterogeneous data sources.
- Ability to translate business problems into technical ML solutions with a strong focus on impact.
- Strong analytical thinking, problem-solving skills, and ability to communicate insights clearly to non-technical audiences.
- Nice to have: experience in public sector projects, fintech, auditing environments, or MLOps practices.
Responsibilities
- Develop and implement machine learning models to predict student dropout risk, enabling early intervention strategies in education systems.
- Design and build fraud detection and anomaly detection models for financial transactions in school-related funding systems.
- Manage end-to-end data science workflows, including data extraction, transformation, modeling, and deployment into production environments.
- Work with large-scale datasets from data lakes and multiple sources, including APIs and internal systems.
- Build and maintain analytical pipelines using cloud-based platforms and machine learning tools.
- Translate complex analytical findings into dashboards, reports, and insights for public sector stakeholders and decision-makers.
- Support model monitoring, documentation, and continuous improvement of deployed solutions.
- Collaborate with technical and business teams to ensure solutions align with real-world policy and operational needs.
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