Data Scientist II, Applied ML
About this role
Data Scientists within the Applied ML group drive AI solutions from inception to deployment to manage risk and improve customer experience. The team builds infrastructure, statistical models, and products using financial data to support decision-making, operational efficiency, and risk management policies across the company.
Practitioners own the entire model development lifecycle, handling problem identification, design, training, productionization, and monitoring. Success in the role requires partnering closely with cross-functional groups such as Operations, Engineering, Product, Fraud, Compliance, and Credit to ensure desired business impacts are realized.
Candidates who thrive here possess strong software engineering fundamentals alongside expertise in statistical techniques and machine learning frameworks. The position suits professionals with a quantitative background who enjoy collaborating with technical and non-technical stakeholders alike.
What you will do
- Drive Data and AI solutions from initial conception through deployment to manage risk and enhance customer experiences.
- Manage the complete machine learning lifecycle including problem identification, model design, training, productionization, and monitoring.
- Partner with cross-functional departments such as Operations, Engineering, Product, Fraud, Compliance, and Credit.
- Apply statistical methods including hypothesis testing and A/B testing with a rigorous analytical mindset.
- Integrate machine learning systems into production services utilizing strong software engineering fundamentals and API development experience.
What you need
- Have three or more years of experience in Data Science or machine learning roles, or two or more years with a PhD in a quantitative field.
- Demonstrate the ability to own end-to-end model development including productionization.
- Possess expertise in Python programming, SQL queries, and machine learning-related frameworks.
- Apply statistical techniques like hypothesis testing and A/B testing to complex problems.
- Bring strong software engineering fundamentals with experience in API development and integrating ML systems into production services.
- Exemplify strong communication skills to collaborate effectively with diverse technical and non-technical stakeholders.
Nice to have
- Experience working with real-time models
- Advanced degree or published research in Machine Learning or a related field
- Previous experience in the risk domain including fraud, AML, and credit
- Experience building customer-facing machine learning models such as suggestions and automations
- Experience in the fintech industry
Benefits
- Equity
Skills and tech stack
Questions candidates ask
What is the expected salary range for this role?
The expected salary range for this role is $152,000 to $190,000 plus equity. However, the starting base pay depends on factors including location, skills, experience, market demands, and internal pay parity.
What qualifications are required for the Applied ML position?
Candidates need three or more years of experience in Data Science or ML roles, or two or more years with a PhD in a quantitative field. Applicants must also demonstrate expertise in Python, SQL, and ML frameworks alongside strong software engineering and communication skills.
Who will I be working with on a regular basis?
Data Scientists partner closely with cross-functional teams across Operations, Engineering, Product, Fraud, Compliance, and Credit. The role involves collaborating with both technical and non-technical stakeholders throughout the model development lifecycle.
What does the model development process look like at Brex?
Data Scientists are responsible for the entire model development lifecycle, starting from conception with stakeholders through model development and productionization. Practitioners follow through to ensure desired business impact is achieved and circle back with stakeholders to make product or strategic decisions.