Braze
Forward-DeployedDataScientistII
Neural analysis suggests this role is
optimal for Mid candidates.
“Forward-Deployed Data Scientist II at Braze. Skills: Machine learning, MLOps, Data science, Customer engagement. Design ML use cases from the ground up. Scope solutions that optimize for business value”
What You'll Achieve.
Optimize for real business value; Deliver model decisions to personalize experiences; Ensure data science performance; Ensure successful adoption; Ensure measurable outcomes
Industry & Context.
Entrepreneurial problem-solver; Troubleshoot obstacles
What They're Looking For.
Must Have
Bachelor's degree in Computer Science, Data Science, Mathematics, Engineering, or a related field, 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role, Experience with large-scale data and production environments, Proficient in Python (Pandas), Proficient in core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost), Skilled in SQL for querying/manipulating datasets, Experience in machine learning pipelines and model deployment, Engineering best practices (Git, CI/CD, testing frameworks, type-hinting, code reviews), Ability to build scalable, maintainable solutions
Nice to Have
Master's or PhD in a relevant technical discipline preferred, Experience in customer-facing or consulting roles strongly preferred, Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), Experience with data integration/ETL, Experience with pipeline optimization, Experience with reinforcement learning algorithms
What You'll Do.
Design ML use cases from the ground up
Scope solutions that optimize for business value
Account for complexity of modern marketing journeys
Proactively identify risks
Build and own the full ML pipeline
Transform customers' raw data
Deliver model decisions to personalize experiences
Drive customer success
Provide ongoing technical guidance
Ensure data science performance
Ensure successful adoption
Ensure measurable outcomes
Extend product capabilities by developing features
Develop tools that support the AI deployment team
Scale what's possible across engagements
Partner with the Braze Product team
Refine Braze's reinforcement learning algorithms
Push self-learning capabilities of the platform forward
Shape BrazeAI product strategy
Shape BrazeAI roadmap
Bring customer-facing insights to the table
Bring deep technical expertise to the table
How You'll Work.
Team & Collaboration
Cross-functional teams; Customer collaborator; Aligning stakeholders; Translating technical concepts
Communication Scope
Translating technical concepts; Clear communicator
Full Job Description
At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew. We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity – inside and outside our organization. To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success. Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture. If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can’t wait to meet you. WHAT YOU'LL DO Our Forward-Deployed Data Scientist team is a group of creative technical experts who design and build end-to-end machine learning solutions that power 1-to-1 personalization for some of the world's leading brands. In this role, you will: 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 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 Drive customer success by providing ongoing technical guidance that ensures data science performance, successful adoption and measurable outcomes Extend product capabilities by developing features and tools that support the broader AI deploymen
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