Experimentation Data Scientist

OVERVIEW

We are seeking a junior to mid-level Experimentation Data Scientist to support the design, execution, and analysis of digital and business experiments for the client, a major player in the travel and hospitality sector. This role sits at the intersection of data science and commercial strategy, supporting experimentation across pricing and revenue management, loyalty programs, and digital/merchandising initiatives. The ideal candidate has strong hands‑on technical skills and a solid grounding in statistical testing, and is looking to grow into more advanced experimentation design work with mentorship from the hiring team.

This is a strong opportunity for someone early in their analytics career who is eager to be trained up rather than someone seeking a senior individual‑contributor role. The client is prioritizing technical aptitude and statistical fundamentals over years of direct A/B testing experience.

• Location:
  
  Seattle, WA (Hybrid – 3 days/week onsite))
• Contract Length: 6 Months to start, Contract-to-Hire opportunity
• Pay Rate: $35–$43/hr

• Partner with commercial stakeholders across pricing/revenue management, loyalty, and digital teams to help design experiment hypotheses, success metrics, sample sizes, and test plans.
• Execute experiment/test plans using established best practices and internal governance standards.
• Interpret experiment results, validate statistical significance, and translate findings into clear, actionable business recommendations.
• Communicate experiment outcomes through presentations and data storytelling that stakeholders across technical and non‑technical audiences can act on.
• Maintain experimentation documentation and contribute to continuous improvement of experimentation processes and consistency.
• Ensure data integrity and quality through data reconciliation, integration, and auditing practices.
• Use core tools including Python, SQL, and Databricks for analysis and data manipulation, with exposure to Optimizely for test execution and Tableau/PowerPoint/Excel for reporting.
• Support experimentation initiatives across a broadening scope of use cases, including pricing/revenue optimization and loyalty program testing, in addition to digital/merchandising experiments.

• Strong hands‑on skills in Python, SQL, and Databricks (this is the top priority for this role).

• Solid understanding of statistics and testing fundamentals (hypothesis testing, significance, sample size considerations, etc.).
• Approximately 1–3 years of experience in analytics, data science, digital optimization, or a related quantitative field.
• Bachelor's degree with a focus on statistics, mathematics, economics, data science, or a related discipline, or equivalent relevant experience in lieu of a degree.
• Strong proficiency in Microsoft Office (Excel, PowerPoint, Word, Outlook) alongside programming skills in SQL and/or Python.
• Excellent communication skills, with the ability to translate technical/statistical findings into clear business recommendations for varied audiences.
• Highly organized, detail‑oriented, and self‑motivated, with the ability to work independently.

• Direct A/B testing or experimentation experience (a plus, but not required if core data/stats skills are strong).

• Familiarity with Optimizely or similar experimentation platforms (nice to have; the client is open to training on this).
• Exposure to pricing, revenue management, or loyalty program analytics.
• Experience in travel, hospitality, retail, e‑commerce, or other consumer‑facing industries.
• Understanding of causal inference or uplift modeling concepts.
• Experience with Python/PySpark and related scientific libraries (e.g., Scikit‑Learn, Num Py, Sci Py).
• Comfort working in Databricks or similar enterprise cloud data platforms.

• Bachelor's degree in statistics, mathematics, data science, economics, or a related field preferred; equivalent practical experience will be considered in lieu of a degree.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other non‑merit factor. We are committed to creating a diverse and inclusive environment for all employees.