Forward-Deployed Data Scientist

About the role

This role guides how machine learning decisions personalize customer experiences for leading brands. The position bridges data science execution and product impact in a fast-growing global team. Success depends on operating with autonomy while aligning to high standards.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

Requirements

Hold a Bachelor's degree in Computer Science, Data Science, Mathematics, Engineering, or a related field; a Master's or PhD in a relevant technical discipline is preferred.

Bring 3-5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments, with experience in customer-facing or consulting roles strongly preferred.

Write well-structured, modular, documented code that follows strong development practices including Git, CI/CD, testing frameworks, type-hinting, and code reviews to build scalable, maintainable solutions.

Demonstrate strong technical expertise with Python (Pandas) and core ML libraries such as TensorFlow, Keras, scikit-learn, CatBoost, and XGBoost, plus SQL for querying and manipulating datasets, machine learning pipelines, and model deployment.

Comfortably work directly with clients and cross-functional teams, aligning stakeholders and translating technical concepts into clear business value.

Practical notes

This role may involve travel, and the team operates with an in-office curated experience designed to foster community.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data scientists in this role work with modern machine learning libraries and deployment tools to solve business problems at scale.

Reinforcement learning and personalization concepts are central to the product and platform direction.

The team values autonomy, accountability, and continuous learning in fast-paced, global environments.

Clear communication skills help translate complex ideas for both technical and non-technical audiences.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Braze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging. ™ Braze helps brands deliver great customer experiences that drive value both for consumers and for their businesses.