Job description

Why join us Brex is the intelligent finance platform that enables companies to spend smarter and move faster in more than 200 markets. By combining global corporate cards and banking with intuitive spend management, bill pay, and travel software, Brex enables founders and finance teams to accelerate operations, gain real-time visibility, and control spend effortlessly. Brex’s AI-native automation and world-class service eliminate manual expense and accounting tasks for customers so they can focus on what matters most. Tens of thousands of the world's best companies run on Brex, including DoorDash, Coinbase, Robinhood, Zoom, Plaid, Reddit, and SeatGeek. Working at Brex allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We’re committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream. We make this a reality by empowering you with the tools, resources, and support you need to grow your career. 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 Compensation The expected salary range for this role is $152,000 - $190,000 + equity. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Brex LLC is a wholly owned subsidiary of Capital One, N.A. Please be aware, job-seekers may be at risk of targeting by malicious actors looking for personal data. Brex recruiters will only reach out via LinkedIn or email with a brex.com domain. Any outreach claiming to be from Brex via other sources should be ignored.

Requirements

Must-haves

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).
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-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythonsqlapi

Platform: greenhouse
Effort: quick