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Bevi is transforming beverage delivery and consumption through IoT-enabled connected machines that eliminate single-use bottles. The company has raised over $160M in venture capital, serves thousands of customers across the US, Canada, UK, and Ireland, and has saved over 1 billion bottles from waste.
As a Product Data Scientist, you will support both the Beverage and Software product teams by measuring the impact of new flavor launches and building recommendation engines that optimize what each machine offers. You'll own analytics across Bevi's proprietary digital experiences, including the UI and the Well platform, plus websites like Bevi.co and Partner.co.
Key responsibilities include:
- Owning analytics for Bevi's digital experiences and web properties, consulting on tracking structure and analyzing what's working to inform the product roadmap
- Measuring the incremental impact of new flavors using A/B testing and quasi-experimental methods (pre/post analysis, difference-in-differences, propensity score matching)
- Building a flavor recommendation engine that identifies optimal flavor profiles per machine while respecting real constraints like weekly-max updates
- Recommending and implementing analytics tools (session replay, self-serve data platforms, testing frameworks, heatmaps) alongside GA4 and Google Tag Manager
- Translating data insights into clear recommendations for non-technical stakeholders
- Building informative dashboards to democratize product data across the organization
- Partnering with data engineering and software teams on data quality, tagging, and instrumentation
You should have 2-4 years of professional experience in product analytics, digital analytics, or data science. Required skills include strong hands-on experience with GA4 and tag management, designing and analyzing experiments, SQL, Python/R, and data visualization tools like Looker, PowerBI, or Hex. You're proactive, spot what needs building before being asked, and drive work to completion independently. You use AI tools in your workflow and think about making product data accessible to both AI and people.
