hybrid
Senior Data Scientist (AI Deployment)
Senior Data Scientist (AI Deployment)
Braze is seeking a Senior Data Scientist specializing in AI Deployment to join their Forward-Deployed Data Scientist group. This role involves collaborating with customers on BrazeAI implementations, extending product capabilities through data pipelines and APIs, and refining reinforcement learning algorithms. The ideal candidate will have over 5 years of experience in data science or ML engineering, strong Python and ML library proficiency, and excellent communication skills.
About the role
WHAT YOU'LL DO
As our customer base continues to grow with the excitement around BrazeAI, we’re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:
- 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
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: 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