Forward-deployed Data Scientist II

Braze Braze · Enterprise · Sao Paulo, Brazil · Customer Experience

The Forward-Deployed Data Scientist II at Braze partners with customers to ensure their success with BrazeAI. This role involves collaborating on implementations, extending product capabilities by improving architecture and developing data pipelines, refining reinforcement learning algorithms, contributing to product strategy, and providing technical expertise for customer adoption and success. The role requires strong Python, ML libraries, SQL, and ML pipeline/deployment experience, with a preference for customer-facing roles and experience with RL algorithms.

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)
  • build scalable, maintainable solutions
  • customer collaborator
  • entrepreneurial problem-solver
  • continuous learner
  • clear communicator

Nice to have

  • DevOps tools (Airflow, Kubernetes, Terraform, GCP)
  • data integration/ETL
  • pipeline optimization
  • reinforcement learning algorithms

What the JD emphasized

  • reinforcement learning (self-learning) algorithms

Other signals

  • improving architecture
  • developing reusable data pipelines, APIs, and components
  • refine and advance our reinforcement learning (self-learning) algorithms
  • shaping BrazeAI product strategy and roadmap
  • customer-facing insights and technical expertise