Senior Data Scientist - Fraud, Reinforcement Learning (Remote) | Career Opportunities | Brex
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Senior Data Scientist - Fraud, Reinforcement Learning (Remote)

Senior Data Scientist - Fraud, Reinforcement Learning (Remote)

Why join us

Brex is reimagining financial systems so every growing company can realize their full potential. As the financial OS, we’re building software and services in one place—disrupting long-entrenched institutions with products and experiences that better serve the ambitions of our customers.

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, developing in a notebook, putting it into production, and circling back with stakeholders to make product or strategic decisions. Our work, in conjunction with our tech, operations, and fraud teams build systems that minimize or prevent fraud

Responsibilities

  • Apply your expertise in quantitative analysis, software engineering to build scalable and robust real-time ML models. 
  • Collaborate with cross-functional teams to unravel complex problems by clearly formulating the problem statement, technical requirements, and present finding at all levels. 
  • Build internal tools or products to enable direct user interaction with the data sets and build ML systems with human-in-the-loop components. 
  • Design, execute, analyze, and interpret the results of experiments across our product.
  • Alongside business stakeholders and engineers, reconcile crucial data integrity issues.
  • Maintain a strong data driven culture within the company by interacting with diverse internal functions.

Requirements

  • 3+ years in a Data Science / ML Engineering / AI Research role
  • Strong engineering skills and practical experience building AI/ML
  • Practical knowledge in building agent-based reinforcement learning models 
  • Comfortable being scrappy and iterating between designs and implementation
  • Comfortable working in an environment where solutions require deep thinking and autonomy. 
  • Interest in modeling the world through simulation
  • Interest in problems that are adversarial in nature

Bonus points

  • Experience implementing multi-agent simulation environments
  • Experience in implementing sequential decision making models

Careers

Senior Data Scientist - Fraud, Reinforcement Learning (Remote)

Senior Data Scientist - Fraud, Reinforcement Learning (Remote)

Why join us

Brex is reimagining financial systems so every growing company can realize their full potential. As the financial OS, we’re building software and services in one place—disrupting long-entrenched institutions with products and experiences that better serve the ambitions of our customers.

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, developing in a notebook, putting it into production, and circling back with stakeholders to make product or strategic decisions. Our work, in conjunction with our tech, operations, and fraud teams build systems that minimize or prevent fraud

Responsibilities

  • Apply your expertise in quantitative analysis, software engineering to build scalable and robust real-time ML models. 
  • Collaborate with cross-functional teams to unravel complex problems by clearly formulating the problem statement, technical requirements, and present finding at all levels. 
  • Build internal tools or products to enable direct user interaction with the data sets and build ML systems with human-in-the-loop components. 
  • Design, execute, analyze, and interpret the results of experiments across our product.
  • Alongside business stakeholders and engineers, reconcile crucial data integrity issues.
  • Maintain a strong data driven culture within the company by interacting with diverse internal functions.

Requirements

  • 3+ years in a Data Science / ML Engineering / AI Research role
  • Strong engineering skills and practical experience building AI/ML
  • Practical knowledge in building agent-based reinforcement learning models 
  • Comfortable being scrappy and iterating between designs and implementation
  • Comfortable working in an environment where solutions require deep thinking and autonomy. 
  • Interest in modeling the world through simulation
  • Interest in problems that are adversarial in nature

Bonus points

  • Experience implementing multi-agent simulation environments
  • Experience in implementing sequential decision making models