GTM Data Scientist

Join Bevi, a company focused on sustainability and innovation in the beverage industry. As a GTM Data Scientist, you will play a crucial role in shaping our go-to-market strategy by analyzing customer behavior, measuring marketing effectiveness, and providing actionable insights to drive growth. You will work closely with Sales, Marketing, and RevOps stakeholders to turn data into a clear point of view on what to do next.

• Construire des modèles prédictifs pour identifier le risque de désabonnement et faire ressortir les opportunités d'upgrade/expansion.
• Mesurer l'impact réel des dépenses marketing à travers l'ensemble de l'entonnoir - numérique et hors ligne.
• Collaborer étroitement avec les parties prenantes des ventes, du marketing et des opérations de revenus pour transformer les données en recommandations claires.
• You use AI tools in your own workflow to move faster (e.g., exploratory analysis, code, documentation), and think about how to make your models and analyses accessible to AI tools as well as people
• 2-4 years of professional experience in data science, applied statistics, or analytics, ideally with exposure to customer/revenue analytics or marketing measurement
• Experience with data visualization tools (e.g., Looker, PowerBI, Hex)
• Hands-on experience building predictive/classification models (e.g., churn, propensity, look-alike) using techniques like logistic regression, gradient boosting, or similar
• A proactive, go-getter mindset - you notice what needs to get built before you're asked, and drive your own work to completion without needing to be chased
• A creative problem solver, comfortable designing a measurement approach when the textbook experiment isn't available
• Experience with causal inference or marketing measurement methods (e.g., MMM, incrementality testing, difference-in-differences) - comfortable incorporating both digital and offline channels (eg events, experiential) into your models, not just clean digital data
• Excellent communication skills - able to translate complex findings into clear, actionable recommendations for non-technical stakeholders
• Strong SQL and Python/R for querying, modeling, and analysis