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 on complex, high-impact data and machine learning challenges in a fast-moving fintech environment.

You will develop models that directly influence critical areas such as credit, fraud, risk, pricing, collections, and user behavior.

The role covers the full data science lifecycle, from exploratory analysis and experimentation to production deployment and model monitoring.

You will work closely with Product, Engineering, Risk, and Operations to turn real business problems into scalable analytical solutions.

The environment encourages hands-on experimentation with modern AI and machine learning techniques, including GNNs and LLMs.

You will have meaningful autonomy to test new approaches, improve models, and contribute to technical best practices.

This role is ideal for a pragmatic, curious data scientist who enjoys learning quickly and translating data into measurable business impact.

Accountabilities

• Develop, test, and continuously improve machine learning models for credit, fraud, risk, and user behavior applications.
• Explore new techniques, features, analytical approaches, and modeling strategies with a strong focus on measurable business impact.
• Own and contribute to the end-to-end machine learning lifecycle, including model design, implementation, deployment, and ongoing maintenance.
• Develop and maintain reliable data and ML pipelines supporting models in production, ensuring stability, scalability, and performance.
• Partner closely with Engineering, Product, Risk, and Operations teams to translate business challenges into effective analytical solutions.
• Conduct exploratory data analysis, experiments, model testing, validation, and performance assessments.
• Monitor business and model performance metrics to identify risks, opportunities, and areas for continuous improvement.
• Contribute to engineering best practices, version control, code quality, and the technical evolution of the data science team.
• Investigate and evaluate emerging machine learning and AI approaches that could improve existing products, processes, or risk systems.

Requirements

• Previous professional experience in Data Science, Machine Learning, Analytics, or a closely related field.
• Strong foundations in statistics, predictive modeling, and data analysis.
• Solid Python development experience and familiarity with relevant machine learning libraries.
• Experience with supervised learning, feature engineering, model evaluation, and analytical experimentation.
• Strong SQL skills and experience working with large-scale datasets.
• Analytical, pragmatic, and hands-on mindset, with the ability to navigate rapidly changing priorities and contexts.
• Strong curiosity and willingness to continuously learn, experiment, and explore new technologies and methodologies.
• Excellent communication skills and the ability to collaborate effectively with technical and business stakeholders.
• Experience in fintech, credit, fraud, risk, or payments is a plus.
• Experience deploying models to production or working with MLOps practices is desirable.
• Familiarity with gradient boosting, neural networks, Graph Neural Networks (GNNs), or Large Language Models (LLMs) is a plus.
• Experience with foundational models and embeddings is desirable.
• Cloud computing experience is a plus.
• Familiarity with Airflow, MLflow, Feast, or similar data and ML tools is advantageous.
• Advanced English proficiency is considered a plus.

Benefits

• Competitive compensation.
• Fully remote work model.
• PJ (independent contractor) engagement.
• Health insurance covering 100% of the employee and 75% of the first dependent.
• iFood Benefits.
• Day off during your birthday month.
• Collective life insurance.
• Discounts with partner stores.
• Opportunity to work on complex, real-world data and machine learning problems.
• Strong exposure to experimentation, modern AI/ML approaches, and high-impact business challenges.
• Collaborative environment with close interaction between technical and business teams.
• Significant autonomy to test ideas and contribute to the evolution of models and products.