Tech Stack

Description

You will collaborate with customer analytics teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration. You will extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components, while working closely with the RL pipeline development team to refine reinforcement learning algorithms. Your customer-facing insights and technical expertise will shape Braze's product strategy and roadmap.

Requirements

• Bachelor's degree in Computer Science, Data Science, Mathematics, Engineering or related field; Master's or PhD preferred
• 3–5+ years hands-on experience as Data Scientist or Machine Learning Engineer with large-scale data and production environments
• Proficient in Python (Pandas) and ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost); skilled in SQL
• Experience with machine learning pipelines, model deployment, and engineering best practices (Git, CI/CD, testing)
• Customer-facing or consulting experience strongly preferred

Responsibilities

• Collaborate with customer Analytics/BI teams 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 with the RL pipeline development team to refine and advance reinforcement learning algorithms
• Contribute to product strategy and roadmap through customer-facing insights and technical expertise
• Provide ongoing technical expertise to ensure successful adoption and long-term customer success