Experimentation Data Scientist

Job Title: Experimentation Data Scientist

Job Type: Contract-to-Hire for 1 to 1.5 year

Location: SeaTac, WA (Hybrid)

Position Overview

We are looking for a Junior Experimentation Data Scientist to support digital experimentation and data-driven decision-making. This role will focus on A/B testing, statistical analysis, data analysis, and experimentation pipelines across areas such as merchandising, loyalty, marketing, and content.

The ideal candidate has a strong foundation in data science, Python, SQL, and statistics, along with strong communication and presentation skills. Experience with Databricks and experimentation is a plus, but candidates with the right technical foundation can be trained on specific tools.

Key Responsibilities

• Analyze data and experimentation pipelines.
• Support and analyze A/B tests and digital experiments.
• Apply statistical concepts to evaluate experiment results.
• Perform data quality and QA checks.
• Analyze experiment outcomes and identify actionable insights.
• Prepare presentations and communicate findings to stakeholders.
• Collaborate with teams across marketing, merchandising, loyalty, and content.
• Support the experimentation process from analysis through presentation of results.
• Learn and apply internal tools and processes with support from the team.

Required Qualifications

• Strong foundation in Data Science, Analytics, or a related field.
• Strong Python skills.
• Strong understanding of statistics and A/B testing.
• Experience with SQL and data analysis.Strong analytical and problem-solving abilities.
• Excellent communication and presentation skills.
• Ability to work effectively with cross-functional teams.

Preferred Qualifications

• Experience with Databricks.
• Experience with experimentation or testing.
• Experience with digital/product analytics.
• Experience with Optimizely or similar experimentation tools.
• Optimizely experience is not required. Candidates with strong data science, Python, and statistical skills can be trained on the tool.

What Makes a Strong Candidate?

The strongest candidates will have a solid data science and statistical foundation, strong Python skills, and experience working with data and experimentation. Extensive Optimizely experience is not necessary; technical and analytical ability is more important than specific tool experience.