Data Scientist II, Applied ML

Brex · São Paulo, São Paulo, Brazil · Data

Posted 2026-08-21

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Data at Brex

The Data organization develops infrastructure, statistical models, and products using financial data. Our Scientists and Engineers work together to make data —and insights derived from data — a core asset across the company. Our work is ingrained in Brex’s decision-making process, in the efficiency of our operations, in our risk management policies, and in the second-to-none experience we provide our consumers.

What You’ll Do

Our Data Scientists are responsible for the entire model development lifecycle, from conception with stakeholders, through model development and productionization, to following through to see that the desired business impact is achieved — including circling back with stakeholders to make product or strategic decisions.

Responsibilities

Drive Data & AI solutions from inception to deployment to efficiently manage risk and/or improve customer experience.

Be responsible for the full machine learning lifecycle — problem identification, model design, training, productionization, and monitoring.

Partner with cross-functional teams (Ops, Engineering, Product, Fraud, Compliance, and Credit).

Requirements

3+ years of experience in Data Science/ML roles, or 2+ years with a PhD in a quantitative field

Demonstrated ability to own end-to-end model development, including productionization

Expertise in Python programming, SQL queries, and ML-related frameworks

Ability to apply statistical techniques such as hypothesis testing and A/B testing, and to approach problems with a statistical mindset

Strong software engineering fundamentals, including experience with API development and integrating ML systems into production services

Strong communication skills and the ability to collaborate with various stakeholders, both technical and non-technical

Nice to Have

Experience working with real-time models

Advanced degree (MSc/PhD) or published research in Machine Learning or a related field

Previous experience in the risk domain (fraud, AML, and/or credit) or building customer-facing ML models (suggestions/automations)

Experience in the fintech industry

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