Forward-Deployed Data Scientist
Company: Braze
Location: Location not specified
Type: Full-time
Posted: 2026-06-02
About this role
### WHAT YOU ’ L L DO
As our customer base continues to grow with the excitement around BrazeAI, we ’ r e expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:
- Collaborate with customer Analytics/BI teams and BrazeAI colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
- Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
- Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
- Contribute to shaping BrazeAI ’ s product strategy and roadmap through customer-facing insights and technical expertise
- Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success
### WHO YOU ARE
- Education: Bachelor ’ s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master ’ s or PhD in a relevant technical discipline preferred
- Experience: 3 – 5 + years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
- Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
- Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
- Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL and ...