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Bevi is a venture-backed IoT beverage technology company on a mission to eliminate single-use bottles and cans. With over $160M in funding and thousands of customers across the US, Canada, UK, and Ireland, Bevi is rapidly scaling its connected beverage platform.
As GTM Data Scientist, you'll sit at the intersection of Marketing and Sales, driving data-informed decisions across customer acquisition, retention, and growth. Your work will directly impact how Bevi allocates resources and prioritizes opportunities.
Key responsibilities include:
• Build predictive models to identify churn risk and surface upgrade/expansion opportunities across the customer base, enabling proactive account management and prioritization.
• Develop look-alike models to identify prospects resembling Bevi's best customers, and own cohort reporting to track segment performance over time.
• Design and execute marketing mix models (MMM) and incrementality analyses to measure true impact of marketing spend across the full funnel—digital and offline channels including events, BeviMobile, and social—informing budget optimization.
• Identify leading indicators and composite metrics for weekly reporting that provide early signals on marketing performance between deeper analytical reads.
• Partner with the Marketing Analytics Engineer to design data structures supporting your modeling work and ensure data accuracy and clarity.
• Translate complex data findings into clear, actionable insights and recommendations for non-technical stakeholders.
You bring 2–4 years of professional experience in data science, applied statistics, or analytics, ideally with exposure to customer/revenue analytics or marketing measurement. You're hands-on with predictive and classification modeling (churn, propensity, look-alike) using techniques like logistic regression and gradient boosting. You have experience with causal inference and marketing measurement methods (MMM, incrementality testing, difference-in-differences), comfortable working with both digital and offline channels. Strong SQL and Python/R skills, proficiency with data visualization tools (Looker, PowerBI, Hex), and creative problem-solving are essential. You leverage AI tools in your own workflow and think about making models accessible to both AI and people. You're proactive, self-directed, and excellent at communicating complex findings to non-technical teams.
