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Salary
$121k – $149k per year
Location
In office (Boston)
Seniority
Junior · 2+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 12, 2026. First seen by Alion on Aug 1, 2026.

Overview
Company
Impact
Profile match
A clean, refreshing office water dispenser and cooler is essential for any workplace. Here’s how Bevi keeps your team hydrated and sustainability goals on track.

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 data and providing insights that drive decision-making. You will work closely with Sales, Marketing, and RevOps stakeholders to identify churn risk, surface expansion opportunities, and measure the impact of marketing spend. This is an exciting opportunity to make a significant impact in a fast-growing company.

Missions

  • 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.

Profil recherché

- 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

- Strong SQL and Python/R for querying, modeling, and analysis

- 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 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

- 2-4 years of professional experience in data science, applied statistics, or analytics, ideally with exposure to customer/revenue analytics or marketing measurement

- 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

- Excellent communication skills - able to translate complex findings into clear, actionable recommendations for non-technical stakeholders

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