Data Scientist, Mid-Level – Credit

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Collaborate with Credit teams, encompassing Policy, Analytics, Monitoring, and Collections, alongside Credit Product teams.

• Independently spearhead Data Science projects, from problem definition and scoping with Product/Operations to production deployment.

• Monitor business impact and consistently enhance solutions.

• Oversee the entire model lifecycle: experimentation, validation, deployment, monitoring, and iteration.

• Establish metrics and monitoring routines for data drift and concept drift.

• Conduct analyses, recognize patterns, and generate insights to enhance the efficiency of Credit operations.

• Create and maintain artificial intelligence agents to assist Product and Business teams in exploring data from Credit products.

• Remain informed about best practices in applied Data Science and development productivity, including AI-assisted development tools.

• Assess and recommend improvements suitable for the context and delivery quality.

⛳️ Requirements

• Practical experience with statistical inference and the application of machine learning models to business challenges.

• Proficient in SQL, Python, and Spark for processing, manipulating, and analyzing extensive datasets.

• Experience in deploying machine learning models to production in both live and batch settings, ensuring operational continuity.

• Knowledge of predictive credit risk models (application, behavioral, or collections), credit metrics, and KPIs, particularly related to credit cards.

• Capability to communicate technical findings and convert statistical metrics into tangible business outcomes.

• Familiarity with structured development methodologies 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: exposure to other credit products, such as Receivables Advances and Loans.

• Preferred: experience with delinquency risk models and credit card limit assignment models.

• Preferred: proficiency in structured hypothesis testing and A/B testing.

• Preferred: knowledge of Causal Inference, including Power Analysis, regression with controls, Instrumental Variables, and Bayesian methods.

• Preferred: familiarity with MLOps frameworks, particularly MLflow and Kedro.

• Preferred: understanding of AWS.

🏝️ Benefits

• Medical and dental insurance with no copay.

• Life insurance.

• Prescription medication assistance.

• Fitness allowance.

• Four complimentary therapy or nutritionist sessions per month.

• Quick massage at headquarters.

• Flexible meal allowance on a Visa card.

• Complimentary 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, in addition to 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.