Forward-deployed Data Scientist

Braze Braze · Enterprise · New York, NY · Customer Experience

The Forward-Deployed Data Scientist at Braze partners with customers to ensure success with BrazeAI. This role involves collaborating on implementations, ML model configuration, data integration, pipeline setup, and extending product capabilities. A key responsibility is working with the RL pipeline development team to refine reinforcement learning algorithms. The role also contributes to BrazeAI product strategy and roadmap through customer insights.

What you'd actually do

  1. Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
  2. Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
  3. Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
  4. Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
  5. Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success

Skills

Required

  • Python (Pandas)
  • core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost)
  • SQL
  • machine learning pipelines
  • model deployment
  • well-structured, modular, documented code
  • strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews)
  • scalable, maintainable solutions
  • customer-facing experience
  • consulting roles
  • working directly with clients
  • translating technical concepts into clear business value
  • identifying opportunities and risks
  • troubleshooting obstacles
  • driving creative solutions
  • staying current with industry trends
  • exploring new tools/technologies
  • explaining complex technical ideas persuasively

Nice to have

  • DevOps tools (Airflow, Kubernetes, Terraform, GCP)
  • data integration/ETL
  • pipeline optimization
  • reinforcement learning algorithms
  • Master’s or PhD in a relevant technical discipline

What the JD emphasized

  • reinforcement learning (self-learning) algorithms

Other signals

  • customer-facing AI product
  • ML model configuration
  • reinforcement learning algorithms
  • BrazeAI product strategy