Cientista de Dados Sênior

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.