Forward-deployed Data Scientist

Braze Braze · Enterprise · Toronto, ON · Customer Experience

The Forward-Deployed Data Scientist at Braze partners with customers to implement BrazeAI solutions, focusing on ML model configuration, data integration, and refining reinforcement learning algorithms. This role extends product capabilities by developing reusable data pipelines and components, and contributes to product strategy through customer insights.

What you'd actually do

  1. Collaborate with customer Analytics/BI teams and BrazeAI 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’s 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
  • TensorFlow
  • Keras
  • scikit-learn
  • CatBoost
  • XGBoost
  • SQL
  • machine learning pipelines
  • model deployment
  • Git
  • CI/CD
  • testing frameworks
  • type-hinting
  • code reviews
  • scalable solutions
  • maintainable solutions

Nice to have

  • Airflow
  • Kubernetes
  • Terraform
  • GCP
  • data integration
  • ETL
  • pipeline optimization
  • reinforcement learning algorithms
  • DevOps tools

What the JD emphasized

  • 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
  • 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

Other signals

  • customer-facing AI implementations
  • ML model configuration
  • reinforcement learning algorithms
  • AI product strategy and roadmap