Data Scientist, AI Deployment

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 personalized 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

What the JD emphasized

• production environments
• reinforcement learning algorithms
• reinforcement learning algorithms

Other signals

• end-to-end machine learning solutions
• personalization for leading brands
• full ML pipeline ownership
• reinforcement learning algorithms
• BrazeAI product strategy