Cientista de Dados Sênior

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Cientista de Dados Sênior based in Brazil.

This is an opportunity to work at the intersection of data science, financial analytics, and credit risk <management.You> will develop statistical and predictive models that support critical business and risk decisions.The role combines exploratory analysis, machine learning, forecasting, and model performance <monitoring.You>’ll work with large corporate datasets to uncover insights and translate them into actionable recommendations.Your work will contribute to expected-loss modeling, credit-risk assessment, budgeting, and financial planning.The environment values continuous improvement, rigorous analysis, collaboration, and <innovation.You>’ll also have the opportunity to apply modern cloud and MLOps practices to impactful financial challenges.

Accountabilities

• Develop and continuously improve statistical and predictive models for key credit-risk indicators, including delinquency, provision expenses, and credit losses.
• Analyze corporate databases and business processes to identify factors that influence credit risk and support more comprehensive risk assessments.
• Enhance statistical modeling methodologies for Expected Credit Loss, incorporating forward-looking analyses to improve the accuracy of credit-risk measurement.
• Perform exploratory data analysis and develop ad hoc models and analyses to address financial-risk challenges across different business areas.
• Conduct model versioning, backtesting, performance monitoring, and continuous validation to ensure the reliability and effectiveness of analytical solutions.
• Prepare reports, analyses, insights, and recommendations for senior leadership and business teams to support strategic decision-making and credit-risk management.
• Maintain and update methodological documentation, procedures, and policies, incorporating changes to existing activities and introducing new analytical processes when required.
• Partner with business and technology stakeholders to understand requirements, translate complex problems into analytical solutions, and communicate findings clearly.
• Contribute to the adoption of agile methodologies and modern data-science practices across risk and financial analytics initiatives.

Requirements

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related technology, quantitative, or exact-sciences field.
• Professional experience developing statistical models and applying data science techniques to complex business problems.
• Strong knowledge of statistics, exploratory data analysis, predictive modeling, and machine learning.
• Solid Python skills for developing analytical and predictive models.
• Experience with SQL and the ability to work effectively with structured and corporate datasets.
• Knowledge of model performance monitoring, validation, and analytical quality practices.
• Familiarity with agile methodologies and collaborative delivery environments.
• Knowledge of AWS, particularly SageMaker, or GCP, particularly Vertex AI, is desirable.
• Strong analytical and problem-solving abilities, with the capacity to translate complex datasets into clear business insights.
• Strong communication skills and the ability to present technical findings and recommendations to both technical and non-technical stakeholders.
• A continuous-learning mindset and interest in applying data science to evolving financial and business challenges.
• Knowledge of credit-risk concepts, particularly IFRS 9, is a strong advantage.
• Experience applying data science to financial problems and familiarity with MLOps environments are considered valuable differentiators.

Benefits

• Health and dental insurance.
• Wellhub (Gympass) access.
• Transportation allowance.
• Meal and food allowances.
• Access to an internal learning platform and partnerships with educational institutions.
• Private pension plan.
• Life insurance.
• Day off.
• Extended maternity and paternity leave.
• Opportunity to work on high-impact data science and financial risk challenges.
• Collaborative and innovation-focused work environment.
• Opportunities for continuous technical and professional development.