The opportunity
At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew.
What you'll do
Design RL use cases from the ground up: scoping solutions that optimize for real business value, accounting for the complexity of modern marketing journeys, 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, so that model decisions are delivered to personalize experiences for millions of end users
Drive customer success by being providing ongoing technical guidance that: ensures data science performance, successful adoption and measurable outcomes
Extend product capabilities by developing features and tools that support the: broader AI deployment team and scale what's possible across engagements
Partner with the Braze Product team to refine and advance Braze's: reinforcement learning algorithms, pushing the self-learning capabilities of the platform forward
Shape BrazeAI product strategy and roadmap by bringing customer-facing: insights and deep technical expertise to the table
What they're looking for
- 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
