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.