Applied ML Data Scientist — End-to-End Production

Brex; San Francisco (CA); Sur place; USD 152 000 - 190 000; Plein temps

Résumé du poste

Brex is seeking a Data Scientist to own the full model development lifecycle from conception with stakeholders to production and monitoring. You will build and deploy real-time ML solutions supporting risk management and customer experience, collaborating with Ops, Engineering, Product, Fraud, Compliance, and Credit.

The role emphasizes strong Python/SQL skills, ML frameworks, and the ability to apply statistical methods like A/B testing in production systems.

Qualifications

• 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 productionisation.
• 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.

Responsabilités

• 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, productionisation, and monitoring.
• Partner with cross-functional teams (Ops, Engineering, Product, Fraud, Compliance, and Credit).

Connaissances

Python

SQL

ML frameworks

Hypothesis testing

API development

Productionisation

Communication

Formation

PhD in quantitative field

MSc in quantitative field

Outils

Docker

MLflow

Spark

Description du poste

Brex is seeking a Data Scientist to own the full model development lifecycle from conception with stakeholders to production and monitoring. You will build and deploy real-time ML solutions supporting risk management and customer experience, collaborating with Ops, Engineering, Product, Fraud, Compliance, and Credit.

The role emphasizes strong Python/SQL skills, ML frameworks, and the ability to apply statistical methods like A/B testing in production systems.