At CloudWalk, we're building the best payment network on Earth (then other planets 🚀). We’re an AI-first fintech unicorn bringing justice to Brazil's broken payment system. We work in a traditional financial sector—but we aim to break conventions with bold, innovative thinking.
We’re looking for a Data Scientist who sees experiments not as tests, but as conversations with reality. You’ll design, run, and analyze credit experiments that shape real-time lending decisions, helping millions of Brazilian entrepreneurs access fairer credit.
The Financial AI Team
We’re part of CloudWalk’s Financial Services domain, powering money movement and credit decisions—including real-time credit engines, repayment orchestration, dynamic pricing, and collections.
We build and run scoring models, underwriting systems, and pricing logic that keep credit decisions fast, fair, and explainable
We push toward event-driven, AI-augmented decisioning where experiments directly shape credit limits, default rates, and merchant growth
We believe in data-driven democratization of access to capital
We put curiosity first—exploring before exploiting
We solve puzzles that demand safety, compliance, explainability, and speed all at once
What You'll Do
Design and execute experiments for credit models, with rigorous frameworks to measure business and merchant impact
Build systematic experimentation infrastructure—metrics, statistical methodologies, and evaluation criteria for credit model performance
Implement A/B testing systems with proper statistical power, randomization, and causal inference methods
Analyze results from multiple model variations, translating them into clear credit policy recommendations
Develop scalable best practices balancing statistical rigor with business speed
Collaborate with engineering to deploy and monitor experimental models in real-time decision engines, with rollback safety nets
Apply measurement science to link experiments to merchant success, default rates, and financial inclusion outcomes
Bridge offline insights to production systems through careful validation and gradual rollout strategies
Technologies / Techniques Used
Python for analysis, modeling, and statistical computing (core language in our stack)
SQL for large-scale feature engineering on financial datasets
Google Cloud Platform + BigQuery for analytics infrastructure
Statistical modeling & experimental design for credit risk evaluation
Machine learning frameworks for classification and risk modeling
MLflow for deployment and monitoring in production
Docker & Kubernetes for orchestration with engineering teams
What You'll Need
Curiosity, initiative, and a bias toward experimenting and learning fast
Statistical rigor: power analysis, bias detection, multiple testing corrections
Python proficiency for analysis, modeling, and statistical computation
Measurement science skills—designing metrics and building robust evaluation frameworks
Experience with machine learning for classification and risk modeling
SQL skills for feature engineering and large dataset analysis
Strong communication skills in English & Portuguese, with ability to explain technical results to non-technical audiences
Nice to Have
Experience with Google Cloud Platform and BigQuery
Hands-on work in credit model experimentation and measurement in production fintech/digital lending environments
MLOps experience—deployment, monitoring, and experimentation at scale
Background or experience in applied statistics or measurement science in business contexts (economics, operations research, etc.)
Recruitment Process Outline
Online Assessment – evaluating theory and logical reasoning
Technical Case Study – working with real-world financial data & experiments
Technical Interview – discussion & case presentation
Cultural Interview – alignment with CloudWalk values
If you are not willing to take an online quiz and work on a test case, do not apply.
Diversity and inclusion:
We believe in social inclusion, respect, and appreciation of all people. We promote a welcoming work environment, where each CloudWalker can be authentic, regardless of gender, ethnicity, race, religion, sexuality, mobility, disability, or education.
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