Design RL use cases from the ground up, optimizing for real business value and proactively identifying risks to set each engagement up for success
Job Summary
Design RL use cases from the ground up, optimizing for real business value and proactively identifying risks to set each engagement up for success.
Build and own the full ML pipeline, taking customers' raw data through transformation, model training, and activation to personalize experiences for millions of end users.
Partner with the Braze Product team to refine and advance Braze's reinforcement learning algorithms, pushing the self-learning capabilities of the platform forward.
Matching Summary
Design RL use cases from the ground up, optimizing for real business value and proactively identifying risks to set each engagement up for success.
Skills & Requirements
Must-have
Python (Pandas)
core ML libraries
SQL for querying
machine learning pipelines
model deployment
well-structured, modular code
strong development practices
Nice-to-have
DevOps tools
data integration/ETL
pipeline optimization
reinforcement learning algorithms
customer-facing experience
consulting roles
entrepreneurial problem-solver
Key Requirements
3–5+ years of experience
Bachelor’s degree required
Master’s or PhD preferred
customer-facing or consulting roles strongly preferred