Cientista de Dados Sênior Mlops (Bilingue - Espanhol)

Accountabilities:

Lead the credit risk modeling workstream for the Argentinean operation, defining technical approaches and recommending appropriate modeling strategies.
Develop application score and behavioral score models throughout the entire lifecycle, from data preparation and exploratory analysis to model development, validation, and final implementation.
Extract, transform, and analyze datasets using SQL and Python to identify relevant patterns, assess data quality, and prepare modeling features.
Apply statistical methods and machine learning techniques to build, compare, and optimize predictive models, including classification, regression, and clustering approaches where appropriate.
Perform feature selection, evaluate model assumptions, and identify the variables that contribute most effectively to credit risk prediction.
Validate model performance and stability before deployment, ensuring that the selected approaches meet business requirements and relevant risk assessment standards.
Monitor deployed models over time, tracking performance, stability, and alignment with expected outcomes.
Identify performance degradation and recommend model recalibration, redevelopment, or other corrective actions when necessary.
Document modeling methodologies, assumptions, validation results, limitations, and recommendations in a clear and structured manner.
Present analytical findings, model performance assessments, and strategic recommendations to credit and risk stakeholders in both Portuguese and Spanish.
Collaborate with business, risk, and technology teams to align modeling initiatives with operational needs and credit decision-making objectives.
Manage the modeling workstream autonomously, prioritizing activities and maintaining high standards of technical quality and delivery.
Requirements:
Bachelor's degree in Statistics or a related quantitative field.
At least five years of professional experience in data science, statistical modeling, or a closely related discipline.
Proven experience in credit risk modeling, with hands-on expertise in both application scoring and behavioral scoring.
Advanced proficiency in SQL and Python for data manipulation, exploratory analysis, feature engineering, and statistical modeling.
Practical experience developing, evaluating, and validating machine learning models, including classification, regression, clustering, and other relevant predictive techniques.
Familiarity with data science and statistical libraries such as pandas, scikit-learn, statsmodels, or equivalent tools.
Advanced Spanish proficiency, including the ability to communicate technical findings, explain model results, and collaborate effectively with Spanish-speaking stakeholders.
Strong analytical thinking and critical reasoning skills, with the ability to challenge assumptions, interpret results, and identify potential limitations in modeling approaches.
Excellent communication skills and the ability to translate complex technical concepts into clear, actionable recommendations for technical and business audiences.
Proven ability to lead a workstream independently, establish priorities, and manage deliverables effectively.
Strong attention to detail, with a commitment to data quality, model documentation, and rigorous validation practices.
Comfort working with international teams and collaborating with stakeholders across different countries and business contexts.
Nice-to-have qualifications:
Master's degree in Statistics or a related quantitative discipline.
Previous experience in banking, credit bureaus, financial institutions, or consulting environments.
Experience monitoring machine learning or statistical models in production, including performance tracking, stability analysis, and model recalibration.
Familiarity with model lifecycle management and MLOps practices for maintaining reliable analytical solutions in production environments.
Benefits:
Fully remote working arrangement, allowing you to work from Brazil.
Full-time professional engagement under a legal entity (PJ) contract.
Opportunity to lead credit risk modeling initiatives for a financial services operation in an international market.
Exposure to end-to-end model development, validation, and monitoring within a business-critical financial domain.
Collaboration with multidisciplinary teams across data science, credit, risk, and technology.
Opportunities to apply advanced statistical methods and machine learning techniques to real-world business challenges.
A collaborative environment focused on technical excellence, continuous learning, innovation, and professional development.