GTM Data Scientist
GTM Data Scientist building predictive churn, look-alike, and marketing mix models to measure campaign impact, identify expansion opportunities, and drive data-informed decisions for Sales, Marketing, and RevOps at a rapidly growing beverage tech company.
About the job
Responsibilities
- Build predictive models to identify churn risk and surface upgrade/expansion opportunities across the customer base to inform proactive outreach and account prioritization.
- Build look-alike models to identify prospects that resemble best customers, and own cohort reporting to track how customer segments perform over time.
- Build marketing mix models (MMM) and incrementality analyses to measure the true impact of marketing spend across the full funnel (digital and offline, including events, BeviMobile, and social) and inform marketing budget optimization decisions.
- Identify leading indicators, including proprietary composite metrics, for weekly reporting that give early signals on marketing performance between full MMM reads.
- Partner with the Marketing Analytics Engineer to inform data structures needed for modeling work and ensure underlying data is accurate and well understood.
- Translate data and analytics into clear insights and recommendations for Sales, Marketing, and RevOps stakeholders.
Requirements
- 2-4 years of professional experience in data science, applied statistics, or analytics, ideally with exposure to customer/revenue analytics or marketing measurement.
- Hands-on experience building predictive/classification models (e.g., churn, propensity, look-alike) using techniques like logistic regression, gradient boosting, or similar.
- Experience with causal inference or marketing measurement methods (e.g., MMM, incrementality testing, difference-in-differences); comfortable incorporating both digital and offline channels into models.
- Strong SQL and Python/R for querying, modeling, and analysis.
- Experience with data visualization tools (e.g., Looker, PowerBI, Hex).
- Creative problem solver comfortable designing measurement approaches when standard experiments are unavailable.
- Proactive, go-getter mindset; spots what needs to be built and drives work to completion without needing direction.
- Excellent communication skills to translate complex findings into clear, actionable recommendations for non-technical stakeholders.
Nice-to-Haves
- Use of AI tools in workflow to move faster (e.g., exploratory analysis, code, documentation) and thinking about making models and analyses accessible to AI tools.
- Exposure to customer/revenue analytics or marketing measurement.
Skills
SQL, Python, R, Marketing Mix Models, Incrementality Testing, Logistic Regression, Gradient Boosting, Looker, Powerbi, Hex
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