Cientista de Dados Sênior Mlops (Bilingue - Espanhol)

Your tasks

• 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.

What we offer

• 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.

How to apply

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