The opportunity
At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew.
What you'll do
Collaborate with customer Analytics/BI teams and Braze 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 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
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
What they're looking for
- Experience: Proven track record 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 pipeline optimization, or reinforcement learning algorithms
