Forward-deployed Data Scientist II

Braze Braze · Enterprise · Paris, France · Customer Experience

The Forward-Deployed Data Scientist II at Braze designs and builds end-to-end machine learning solutions for customer personalization. This role involves scoping ML use cases, building and owning the full ML pipeline from data transformation to model training and activation, driving customer success through technical guidance, extending product capabilities, and partnering with the Product team to advance reinforcement learning algorithms and shape AI product strategy.

What you'd actually do

  1. Design ML 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
  2. 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
  3. Drive customer success by providing ongoing technical guidance that ensures data science performance, successful adoption and measurable outcomes
  4. Extend product capabilities by developing features and tools that support the broader AI deployment team and scale what's possible across engagements
  5. Partner with the Braze Product team to refine and advance Braze's reinforcement learning algorithms, pushing the self-learning capabilities of the platform forward

Skills

Required

  • Python (Pandas)
  • TensorFlow
  • Keras
  • scikit-learn
  • CatBoost
  • XGBoost
  • SQL
  • machine learning pipelines
  • model deployment
  • well-structured, modular, documented code
  • Git
  • CI/CD
  • testing frameworks
  • type-hinting
  • code reviews
  • scalable, maintainable solutions

Nice to have

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

What the JD emphasized

  • customer-facing
  • production environments
  • reinforcement learning algorithms

Other signals

  • customer-facing ML solutions
  • end-to-end ML pipeline
  • reinforcement learning algorithms
  • BrazeAI product strategy