Staff Data Scientist, AI

Engineering at Brex

The Engineering team includes Data, IT, Security, and Software, and is responsible for building innovative products and infrastructure for both internal and external users. We have multiple autonomous and collaborative teams who are eager to learn, teach, and constantly improve how things work. Together, we strive to build robust and scalable systems that enable Brex to grow rapidly and help our customers reach their full potential.

What you’ll do

As a Staff Data Scientist in the AI team, you will significantly impact Brex’s spend management offering by leveraging cutting-edge AI/ML techniques to drive automation and enhance customer experience. You will own the full lifecycle of AI/ML initiatives, from initial ideation and research to development, production deployment, and continuous improvement, and leading our data science function into a world that leans harder on third party foundational LLMs like Claude and ChatGPT. This role requires a strategic and tactical approach, balancing technical execution with cross-functional collaboration to prioritize and deliver high-impact solutions. You will play a crucial role in shaping the future of Brex’s AI-powered products.

Where you’ll work

This role will be based in our Seattle office. You must be willing to work in office at least 2 days per week on Wednesday and Thursday. Employees will be able to work remotely for up to 4 weeks per year.

Responsibilities

• Lead the design and implementation of complex spend management solutions using advanced ML and AI techniques, including large language models (LLMs).
• Partner closely with product managers, designers, and engineers to identify critical customer pain points and strategic opportunities for AI/ML development and deployment.
• Serve as a technical expert and thought leader, consulting and educating internal teams on emerging research, technologies, and best practices in leveraging ML and LLMs for product design and development.
• Drive continuous improvement of the AI platform by collaborating with AI Platform engineers to define future requirements and evolve the platform's capabilities.
• Mentor and provide technical guidance to junior and mid-level data scientists, fostering a culture of innovation and excellence within the team.

Requirements

• 5+ years of industry experience developing and deploying advanced optimization and machine learning models that have demonstrated significant business impact.
• M.S. or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field.
• Expert proficiency in programming languages (e.g., Python) and extensive experience with machine learning libraries (e.g., SciKit Learn, TensorFlow, PyTorch).
• Deep expertise in natural language processing (NLP) techniques, including experience with various LLM architectures and applications.
• Strong experience in big data analysis, system design, and backend integration with ML/LLM systems and solutions.
• Exceptional understanding of quantitative disciplines such as statistics, machine learning, operations research, and causal inference.
• Proven track record of productionizing and A/B testing sophisticated machine learning models in real-world applications.
• Demonstrated experience leveraging commercial or open-source LLM APIs (e.g., Anthropic, OpenAI, Llama) to build innovative solutions.
• Must be willing to work in office 2 days per week on Wednesday and Thursday.

Bonus points

• Experience leading complex technical projects from conception to production.
• Prior experience in the finance industry or with spend management solutions.
• Active contributor to the data science community through publications, open-source projects, or conference presentations.
• Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.

Compensation

The expected salary range for this role is $240,000 - $300,000. 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. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.