XM Scientist - Schematization and Benchmarking

Job Description

The Challenge

As a new team in Qualtrics, we are looking for a scrappy Experience Management (XM) Scientist who wants to make their mark on the XM platform by creating an exceptional data catalog and set of benchmarks. You’ll be a trusted advisor to the engineering teams, product teams, and sales teams as they bring the power of Qualtrics data to the world.

We are looking for an applied social scientist with a PhD and/or 2+ year track record of demonstrating deep expertise in a scientific discipline relevant to experience management (e.g., Survey Methodology, Marketing Research including brand & communications, Econometrics, Political Science, Sociology, IO Psychology, Statistics & Data Science).

Primary Responsibilities

• Serve as the guiding light for building the semantic layer of Qualtrics data - create and enforce best practices in the data dictionary, ensuring wide usage and high quality.
• Create and facilitate new industry benchmarks that will make the greatest difference to Qualtrics customers. Create and facilitate the policies and best practices that ensure reliable benchmarks.
• Inform and sculpt product strategy in creating increasingly automated and useful Qualtrics benchmarks.Consult with sales, product managers, and beyond on how benchmarks are created and what makes a great benchmark.
• Develop training materials and learning experiences that enable others to quickly gain knowledge and technical know-how across a wide range of areas in data schematization and benchmark creation and usage.

Preferred Qualifications

• Degree in the social sciences or other disciplines closely related to Experience Management (e.g., Survey Methodology, Marketing Research, Econometrics, Political Science, Sociology, IO Psychology, Statistics & Data Science)
• PhD and/or 2+ years of applied research experience
• Ability to communicate clearly and answer methodological or technical questions directly without unnecessary jargon
• Preferred proficiency with a minimum of one statistical programming language such as Python, R, Stata, with the ability to provide high quality peer review of common statistical tests and assumptions as they relate to research designs
• Understanding of high-quality benchmark (comparison) design and execution