Data Scientist, Mid-Level – Credit

• Collaborate with Credit teams, including Policy, Analytics, Monitoring, and Collections, as well as Credit Product teams
• Independently lead Data Science projects, from defining and scoping the problem with Product/Operations through production deployment
• Track business impact and continuously improve solutions
• Manage the full model lifecycle: experimentation, validation, deployment, monitoring, and iteration
• Define metrics and monitoring routines for data drift and concept drift
• Conduct studies, identify patterns, and generate insights to improve the efficiency of Credit operations
• Develop and maintain artificial intelligence agents to help Product and Business teams explore data from Credit products
• Stay up to date on best practices in applied Data Science and development productivity, including AI-assisted development tools
• Evaluate and propose improvements appropriate to the context and delivery quality

• Hands-on experience with statistical inference and applying machine learning models to business problems
• Proficiency in SQL, Python, and Spark for processing, manipulating, and analyzing large datasets
• Experience deploying machine learning models to production in both live and batch architectures, and keeping them operational
• Familiarity with predictive credit risk models (application, behavioral, or collections), credit metrics and KPIs, with a focus on credit cards
• Ability to communicate technical results and translate statistical metrics into practical business impact
• Familiarity with structured development practices and code version control (Git)
• Completed degree or equivalent practical experience in Computer Science, Engineering, Statistics, Mathematics, Physics, or related fields
• Preferred: experience with Databricks (Model Serving, Unity Catalog, and Genie Spaces)
• Preferred: experience with other credit products, such as Receivables Advances and Loans
• Preferred: experience with delinquency risk models and credit card limit assignment models
• Preferred: experience with structured hypothesis testing and A/B testing
• Preferred: experience with Causal Inference, including Power Analysis, regression with controls, Instrumental Variables, and Bayesian methods
• Preferred: experience with MLOps frameworks, especially MLflow and Kedro
• Preferred: familiarity with AWS

• Medical and dental insurance with no copay
• Life insurance
• Prescription medication assistance
• Fitness allowance
• Four free therapy or nutritionist sessions per month
• Quick massage at headquarters
• Flexible meal allowance on a Visa card
• Free food at headquarters
• Childcare assistance
• Parental support program
• Extended maternity and paternity leave
• In-house training platform
• Education assistance covering 70% of undergraduate degree and language tuition, as well as courses and books
• Home office allowance
• Work equipment
• Furniture allowance
• Partnership with WOBA for coworking access throughout Brazil
• Day off during your birthday month
• Happy Hour allowance
• Referral bonus for new hires
• Annual performance-based bonus
• Stock options plan
• No dress code