Statistician - E-Commerce

Hiring organization: Confidential — Confidential e-commerce company (employer named before interviews)
Location: Overland Park, KS, US
Workplace: On-site
Employment: Full-Time
Pay: $90,000 - $121,500 per year
Date posted: 2026-10-06

Job description

Role overview: The Statistician - E-Commerce is based in Overland Park, Kansas and supports online orders, checkout journeys, and post-purchase service. This position combines hands-on execution with reliable documentation and collaboration across operational and technical teams. Responsibilities: Design analytical studies, evaluate data quality, select statistical methods, and communicate uncertainty. Apply these responsibilities to online orders, checkout journeys, and post-purchase service. Work with process owners to track order exceptions and evaluate changes against customer completion rates. Identify problems early, record the evidence behind recommendations, and coordinate handoffs so agreed work is completed accurately. Qualifications: 2+ years of relevant professional experience or equivalent demonstrated project work. Demonstrated capability in statistical inference, experimental design, R or Python, and reproducible analysis. Experience supporting e-commerce is preferred. Ability to document decisions, communicate clearly, and handle business information responsibly. Initial priorities: Produce a reproducible analysis with documented assumptions and uncertainty. Begin by reviewing the current process, agreeing on a baseline with stakeholders, and identifying the most useful improvement to deliver. Report results using measures appropriate to the role and explain any unresolved dependencies. Work arrangement: Full-Time, On-site in Overland Park, Kansas. Estimated annual cash compensation: USD 90,000 to 121,500. This illustrative range is centered on a national occupational benchmark; local pay, seniority, incentives, and benefits require employer review. No benefits or incentive plan is asserted.

Required skills

statistical inference, experimental design, R or Python, and reproducible analysis

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