Forward-Deployed Data Scientist

Responsibilities

• 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

Requirements

• 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

Benefits

Additional Information

At Braze, we have found our people. We're a genuinely approachable, exceptionally kind, and intensely passionate crew. We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity - inside and outside our organization. To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success. Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture. If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can't wait to meet you.

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