Staff Data Scientist - Product

Role Overview

As a Staff Data Scientist within the Product Data Science & Engineering team, you will oversee the complete scientific roadmap for a specific product domain. This involves close partnership with Product, Engineering, and Design teams to define problems, establish success metrics, and manage experiments from initial discovery through decision-making to iterative improvements. You will lead the measurement strategy, pioneer AI applications in analytics and autonomous use cases, and serve as a trusted expert to senior leaders tackling complex challenges.

Broader Impact

Beyond your own domain, you will create reusable analytics assets adopted across multiple pods, mentor growing data scientists, and contribute to setting standards and tooling for the wider data science community within the company.

Team Context

The Product Data Science & Engineering team closely collaborates with Product, Engineering, and Design units across the company’s product and technology divisions, facilitating decision-making, experimentation, and measurement. The team is evolving from a service-oriented analytics group into a product-driven function, focusing increasingly on AI-powered analytics experiences and autonomous solutions with a strong customer and design focus.

Current Focus Areas

• Defining measurement, evaluation metrics, and safety guardrails for AI-enabled product features and autonomous experiences
• Collaborating with engineering to productionize decision, ranking, and risk models
• Developing and expanding reusable analytics assets, including canonical metrics and experimentation tools
• Implementing AI and automation to improve internal team workflows

Work Environment

The company supports a flexible hybrid approach that encourages a balance between in-office collaboration and remote work autonomy. Employees have access to modern office spaces with expectations for office days and collaborative days to enhance team connectivity and productivity.

Candidate Profile

• Strong proficiency in SQL and Python coupled with solid statistical knowledge, including expertise in experimentation and causal inference methods
• Comfortable using Snowflake, dbt, and Git, and familiar with continuous integration/continuous deployment (CI/CD) practices
• Experience in deploying predictive or decision models in production environments alongside engineering teams, including applications in scoring, risk assessment, ranking, or propensity analysis
• Adept at influencing senior stakeholders across Product, Engineering, and Design, aligning them on decision criteria and trade-offs
• Passionate about mentoring and coaching fellow data scientists
• Holds a tertiary qualification in a quantitative discipline with extensive experience in data science or applied sciences

Diversity and Hiring Philosophy

The company encourages applications even if the candidate’s experience is not an exact match, valuing skills, enthusiasm, and unique perspectives that can positively influence culture and team dynamics.