Data Scientist II, Applied ML

Requisitos

• **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