Data Scientist II, Infrastructure

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Company Overview

A leading global visual discovery platform connects millions of people with creative ideas and possibilities. Its technology ecosystem supports a large-scale platform where reliability, performance, measurement, and efficiency are critical to delivering a strong user experience.

The organization is also investing heavily in artificial intelligence, using AI as a partner to augment creativity, improve productivity, and increase impact. Employees are encouraged to demonstrate strong foundational skills, thoughtful problem-solving approaches, and the ability to work effectively alongside AI technologies.

The organization is seeking a Data Scientist II, Infrastructure to join its Infrastructure Data Science team. This role will partner closely with engineering and cross-functional teams to make complex infrastructure systems more measurable, understandable, and actionable.

Depending on the area of focus, the role may span application performance, shopping infrastructure, metrics quality, infrastructure governance, or site reliability. The successful candidate will help establish data foundations, measurement systems, and analytical frameworks that enable better technical and business decisions.

Key Responsibilities

• Partner with engineering teams to define, measure, and improve the health, quality, and efficiency of infrastructure systems.
• Build and refine metrics, dashboards, and analytical frameworks that make complex technical systems easier to understand and act upon.
• Strengthen data foundations by improving metric definitions, auditing data quality, and contributing to pipeline and measurement improvements.
• Design and analyze experiments, investigations, and deep dives to quantify the impact of infrastructure changes on user experience, reliability, and business outcomes.
• Translate ambiguous technical problems into structured analyses and actionable recommendations for engineering and platform stakeholders.
• Support high-priority investigations and decision-making related to infrastructure performance, reliability, cost, and measurement quality.
• Identify opportunities to improve infrastructure measurement and optimization across areas such as performance, shopping infrastructure, governance, metrics quality, and site reliability.
• Collaborate with cross-functional stakeholders to establish reliable and scalable measurement foundations.
• Contribute to production-ready data pipelines, measurement systems, and foundational data initiatives that improve data quality and usability.

Required Qualifications

• Master’s degree in a relevant field such as Statistics, Applied Mathematics, Biostatistics, or equivalent experience.
• Strong SQL and analytical programming skills.
• Experience working with complex, messy, or imperfect datasets and developing reliable metrics and datasets.
• Experience contributing to production-ready data pipelines, measurement systems, or foundational data initiatives.
• Strong understanding of experimentation and measurement methodologies.
• Ability to design rigorous analyses and interpret results effectively.
• Demonstrated ability to translate ambiguous problems into clear analytical workstreams and actionable recommendations.
• Strong cross-functional communication skills with the ability to explain technical findings to engineering, product, and platform stakeholders.
• Ability to work independently, prioritize multiple projects, and respond effectively to fast-moving requests.
• Strong curiosity and a builder mindset, with an interest in improving complex systems and creating scalable, trustworthy measurement foundations.

AI and Interview Approach

The organization values candidates who can clearly explain their problem-solving approach, including how they think about and collaborate with AI. The interview process evaluates foundational technical and analytical skills alongside the ability to reason through problems and communicate decisions effectively.

Work Environment

The organization recognizes that the ideal working environment can vary depending on the department and role. Day-to-day working arrangements may differ based on organizational and business needs.

Disclaimer

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