Position Details
About this role
Data Scientist focusing on customer-facing BrazeAI implementations, collaborating with Analytics/BI teams to define use cases, build data pipelines, configure ML models, and advance reinforcement learning algorithms in production.
Key Responsibilities
- Collaborate with customer Analytics/BI teams on implementations
- Extend product capabilities with reusable data pipelines, APIs, and components
- Refine reinforcement learning algorithms with RL pipeline development team
- Contribute to BrazeAI product strategy and roadmap through customer-facing insights
- Provide ongoing technical expertise to ensure successful adoption and measurable outcomes
Technical Overview
Technical scope includes Python-based ML stack (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost), SQL, data pipelines, APIs, and deployment tooling (Git, CI/CD, Airflow, Kubernetes, Terraform) on Google Cloud Platform.
Ideal Candidate
The ideal candidate is a data scientist with 3+ years of hands-on ML experience, strong Python and ML library background (TensorFlow, Keras, scikit-learn), plus SQL and data-pipeline experience. They should be comfortable in a customer-facing role, collaborating with clients on BrazeAI implementations and deploying ML models into production.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Keywords for Your Resume
Deal Breakers
3+ years hands-on ML experience, Strong Python (Pandas) and ML library proficiency, Experience deploying models to production, Onsite in New York, NY
Get matched to jobs like this
Luna finds roles that fit your skills and career goals — no endless scrolling required.
Create a Free Profile