What You'll Own:

• Analyze A/B tests end-to-end - what worked, what didn't, why? and deliver decision-ready recommendations, not metrics readouts.
• Map user journeys across devices and traffic sources; pinpoint friction, drop-offs, and conversion leaks; convert them into prioritized, testable hypotheses.
• Dig into data at segment, device, source, and journey-step level to find the non-obvious drivers that top-line reporting misses.
• Build and maintain your own data pipelines across Shopify, GA4, Google Ads, and BigQuery - you don't wait in a queue to get the data you need.
• Contribute to a structured hypothesis pipeline: prioritize by impact, write clear test briefs, define success criteria upfront, and push proposals to execution.
• Orchestrate agentic AI workflows to accelerate analysis using SQL and Python as the foundation to direct, validate, and scale your analytical output.
• Collaborate with marketing, UX, and development to translate findings into site changes, campaign adjustments, and creative decisions.

What You Bring:

• 3+ years in data analysis, experimentation, or CRO within e-commerce, D2C, or any high-traffic transactional website (fintech, marketplaces, SaaS, banking).
• Strong SQL - complex joins, window functions, CTEs across large datasets without relying on pre-built dashboards.
• Working Python (or R) for data manipulation, statistical analysis, and automation.
• Proven A/B testing experience - hypothesis design through statistical analysis to business recommendation; solid grasp of sample sizing, significance thresholds, and common pitfalls.
• Hands-on GA4 experience - event-based tracking, custom explorations, and the ability to tie GA4 data to other sources.
• Experience integrating ad platform data (Google Ads, Meta) with site analytics to see the full acquisition-to-conversion funnel.
• Ability to independently connect to and pull from multiple data sources (APIs, warehouses, flat files) without engineering support.
• Sharp analytical communication - complex findings distilled into concise recommendations for non-technical stakeholders.
• Proactive and curious - you spot testing opportunities and chase root causes before anyone asks.

Good-to-Have:

• Experience with BigQuery or AWS data warehousing, including scheduled queries and data modeling.
• Familiarity with session replay and heatmap tools (FullStory, Hotjar) for qualitative behavioral analysis.
• Background in Looker, Tableau, or similar dashboarding tools.