Senior Data Scientist

About the role

Join our Forward-Deployed Data Scientist group to help clients maximize the value of the BrazeAI platform. You will serve as a technical partner to our customers, bridging the gap between complex machine learning capabilities and practical business outcomes. In this capacity, you will translate ambiguous business goals into precise analytical strategies and technical workflows. You will act as a trusted advisor, guiding clients through the implementation of advanced machine learning features within the Braze ecosystem. Your work will directly shape how customers leverage predictive analytics and orchestration to drive measurable business impact. You will collaborate closely with product teams to ensure that deployed solutions are robust, scalable, and aligned with user needs. This role requires a proactive mindset and the ability to deconstruct complex problems into actionable analytical steps. You will contribute to the evolution of the platform by feeding real-world insights back into product and engineering discussions.

Key facts

What you'll do

• Partner with customer Analytics and BI teams to define use cases, manage data integration, and configure machine learning models within the Braze platform.
• Design, develop, and maintain reusable data pipelines, APIs, and software components to enhance the capabilities of the BrazeAI platform.
• Work alongside the reinforcement learning pipeline development team to test, iterate, and advance self-learning algorithms in production settings.
• Analyze customer interaction data to identify patterns and opportunities for optimization, influencing the strategic direction of BrazeAI features.
• Shape the BrazeAI product roadmap by translating technical findings and customer feedback into actionable recommendations for the product team.
• Provide technical leadership to ensure long-term customer success, guiding clients on best practices for model deployment and performance measurement.
• Deliver clear and structured documentation for implemented solutions, ensuring that technical artifacts support maintainability and knowledge transfer.
• Communicate complex analytical concepts and model behaviors to both technical and non-technical stakeholders using clear and precise language.
• Support the evaluation and validation of machine learning experiments, ensuring that results are reproducible and aligned with client objectives.
• Act as an internal subject matter expert, mentoring junior team members and elevating the overall quality of technical engagements.

Requirements

• Hold a Bachelor degree in Computer Science, Data Science, Mathematics, Engineering, or a related field.
• Bring a minimum of 5 years of professional experience in Data Science or Machine Learning Engineering focused on large-scale production environments.
• Demonstrate proficiency in Python, specifically using Pandas, alongside core machine learning libraries such as TensorFlow, Keras, scikit-learn, CatBoost, and XGBoost.
• Exhibit advanced SQL capabilities for complex data querying, transformation, and optimization across large datasets.
• Show strong engineering discipline by consistently using Git, CI/CD pipelines, testing frameworks, type-hinting, and modular code documentation.
• Communicate complex technical concepts effectively to diverse audiences, including both technical specialists and business stakeholders.
• Thrive in a fast-paced, collaborative environment where priorities may shift based on evolving customer needs and project demands.
• Maintain a high standard of quality and attention to detail when delivering analytical solutions that impact customer-facing products.
• Navigate ambiguous requirements and translate them into structured analytical plans and technical specifications.
• Adhere to data privacy and security guidelines in all aspects of model development and client interaction.

Nice to have

• Hold a Master degree or PhD in a technical discipline.
• Have prior experience in consulting or customer-facing technical roles where you engaged with diverse business problems.
• Demonstrate familiarity with DevOps tools such as Airflow, Kubernetes, Terraform, and GCP for deploying and managing scalable data workflows.
• Bring a background in pipeline optimization, ETL processes, or reinforcement learning applied to real-world systems.
• Show evidence of contributions to open source projects or technical communities that align with data science and machine learning practices.
• Possess experience working in global environments with cross-functional teams across different time zones and cultural contexts.

Practical notes

• This role operates under a Hybrid engagement model, balancing remote and in-office work in São Paulo.
• Compensation includes a competitive salary and potential equity components based on performance and tenure.
• Benefits encompass flexible paid time off, comprehensive health plans covering medical, dental, and vision, along with life and disability insurance.
• The company provides family services and allocates a yearly learning stipend to support professional development.
• Candidates may request manual review or opt out of AI-assisted application processes by contacting talentdata.privacy@braze.com.
• The company utilizes AI-assisted tools for application screening and administrative tasks; applicants are encouraged to reach out with questions regarding these systems.
• No specific travel requirements are indicated for this position at this time.
• Visa sponsorship details are not provided and should be clarified during the hiring process if applicable.
• Deadlines for submission of applications are not specified; interested candidates are advised to apply as soon as possible to ensure full consideration.