Forward-Deployed Data Scientist II

Braze

Sydney

4 weeks ago

Sydney

4 weeks ago

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Forward-Deployed Data Scientist II

Forward-Deployed Data Scientist II role at Braze in Sydney, focusing on designing and building end-to-end machine learning solutions for customer personalization. Requires 3-5+ years of data science experience, proficiency in Python and ML libraries, and strong engineering practices. Offers competitive compensation, flexible benefits, and a collaborative, inclusive culture.

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Hybrid

Full-time

Mid Level

Python

Pandas

Salary

Not specified

Work Location

Sydney, New South Wales, Australia, AU

Work Model

Hybrid

Experience Required

5 years

Employment Type

Full-time

Experience Level

3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role

Core Qualifications

Technical (Must-have)

PythonPandasTensorFlowKerasscikit-learnCatBoostXGBoostSQLmachine learning pipelinesmodel deployment

Soft Skills

customer collaborationcross-functional teamworkstakeholder alignmententrepreneurial problem-solvingcontinuous learningclear communication

Tools (Must-have)

GitCI/CDtesting frameworks

Preferred Qualifications

Technical (Nice-to-have)

AirflowKubernetesTerraformGCPdata integrationETLpipeline optimizationreinforcement learning algorithms

Key Responsibilities

•Design RL 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 being 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 deployment team and scale what's possible across engagements
•Partner with the Braze Product team to refine and advance Braze's reinforcement learning algorithms, pushing the self-learning capabilities of the platform forward
•Shape BrazeAI product strategy and roadmap by bringing customer-facing insights and deep technical expertise to the table

Data ScientistMachine LearningAI DeploymentPythonReinforcement LearningCustomer EngagementSoftware DevelopmentEngineeringHybrid WorkMid Level

Core Qualifications

Technical (Must-have)

PythonPandasTensorFlowKerasscikit-learnCatBoostXGBoostSQLmachine learning pipelinesmodel deployment

Soft Skills

customer collaborationcross-functional teamworkstakeholder alignmententrepreneurial problem-solvingcontinuous learningclear communication

Tools (Must-have)

GitCI/CDtesting frameworks

Key Responsibilities

•Design RL 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 being 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 deployment team and scale what's possible across engagements

•Partner with the Braze Product team to refine and advance Braze's reinforcement learning algorithms, pushing the self-learning capabilities of the platform forward

•Shape BrazeAI product strategy and roadmap by bringing customer-facing insights and deep technical expertise to the table