WHAT YOU'LL DO
Our Data Scientist, AI Deployment team is a group of creative technical experts who design and build end-to-end machine learning solutions that power 1-to-1 personalization for some of the world's leading brands. In this role, you will:
- 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
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 pipeline optimization, or reinforcement learning algorithms
- Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
- Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
- Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
- Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences