- Experience
- Any
- Salary
- —
- Openings
- 1
- Posted
- 1 hour ago
- Work mode
- Work from home
- Education
- Tertiary qualification in a quantitative field
- Resume
- Required to apply
Sign in to tell us what does and doesn't work for you here — it sharpens every match we show you.
Job description
About the Role
As a Staff Data Scientist within the Product Data Science & Engineering team, you will take full ownership of scientific roadmaps for a specific product domain. Collaborating closely with Product, Engineering, and Design teams, you will identify key problems, establish success criteria, and drive experimentation through discovery, decision-making, and iterative processes. You will lead measurement strategies specific to your domain, influence how AI integrates with analytics and agentic use cases, and serve as a trusted advisor to senior stakeholders addressing the most complex challenges.
Team Overview
The Product Data Science & Engineering team partners extensively with Product, Engineering, and Design groups across the company, supporting data-driven decision-making, experimentation, and measurement initiatives. Transitioning from a service-based analytics group to a product-centric function, the team is increasingly responsible for designing and deploying AI-powered analytics solutions including agentic experiences. Operating within a design-driven environment, the team prioritizes customer and product focus in all developments.
Key Projects
- Developing measurement frameworks, evaluation metrics, and safety guardrails for AI-driven product features and agentic interactions.
- Collaborating with engineering to implement decision, ranking, or risk models in production.
- Creating and expanding reusable analytical components such as canonical metrics and tools for experimentation.
- Implementing AI and automation strategies to streamline the team's workflows.
Working Environment
The company supports a flexible hybrid model balancing remote work autonomy and in-office collaboration. Employees have access to modern office spaces with designated office and collaboration days designed to encourage team connection and efficient workflow.
Desired Candidate Profile
- Proficient in SQL and Python with strong foundations in statistics, including expertise in experimentation and causal inference methods.
- Experienced with Snowflake, dbt, and Git, comfortable with continuous integration and deployment practices.
- Track record of successfully deploying production models or decisioning systems such as scoring, risk, ranking, or propensity models in partnership with engineering teams.
- Skilled at influencing senior stakeholders across product, engineering, and design to reach alignment on decisions, criteria, and trade-offs.
- Passionate about mentoring and coaching data scientists beyond just fulfilling requirements.
- Holds a tertiary degree in a quantitative field with substantial experience in applied or data science.
Additional Information
Applicants with varied backgrounds are encouraged to apply as the hiring decision is based on individual capabilities, enthusiasm, and unique contributions to company culture and the team.