Role overview
Join us in revolutionizing the food-tech industry and become a part of one of the fastest-growing restaurant-discovery platforms.
The mission of the Marketing Data Scientist is to build NeoTaste’s marketing measurement and optimization engine, enabling us to reliably attribute acquisition, model incrementality, and to continuously optimize CAC versus LTV across channels, cities, and cohorts.
You will turn complex, imperfect data into decision-grade insights that actively shape how we allocate budget, launch cities, and scale growth. You will:
What you'll work on
- Build a trustworthy, decision-ready view of marketing performance across channels, campaigns, creatives, geographies, and user cohorts.
- Design, build and continuously improve NeoTaste’s Marketing Mix Model (MMM) to support budget allocation, incrementality estimation and marginal ROI analysis.
- Establish clear and consistent measurement standards for CAC, LTV, payback periods and cohort-based performance tracking.
- Integrate and reconcile data from multiple sources, including MMPs, ad platforms, app analytics, subscription and revenue backends, CRM systems, and our own data sources.
- Identify high-signal product events that predict conversion, retention and long-term value, and translate them into actionable marketing and CRM levers.
- Enable data-backed budget decisions by clearly communicating what is incremental, what is uncertain, and where spend should be increased or reduced.
- Partner closely with the wider Marketing, Product, Engineering Teams to improve tracking architecture, event schemas and data joinability.
- Continuously improve NeoTaste’s measurement maturity, turning data quality and experimentation into a long-term competitive advantage.
What we're looking for
- You’re a hands-on data scientist with a strong marketing mindset: You enjoy working on messy, real-world data problems and take ownership of building reliable truth.
- You’re technically strong: You work confidently with SQL, Python or R, data modeling, and complex joins across multiple data sources.
- You understand marketing economics: You have a deep grasp of CAC, LTV, cohorting, payback and attribution limitations - especially in an iOS-privacy-driven world.
- You combine product analytics with data science: You can identify predictive signals in user behavior and translate them into concrete marketing actions.
- You’re comfortable with causal thinking: You can design incrementality approaches using MMMs, experiments, geo-lift or holdouts and clearly explain trade-offs.
- You influence decisions: You make analysis legible, trusted and actionable, and you’re comfortable pushing back when metrics are misleading.
- You’re proactive about data quality: You don’t just report problems, you challenge schemas, propose improvements and work with Engineering to ship fixes.
- Bonus points for: having built a Marketing Mix Model end-to-end in a previous role and/or have experience in subscription-based, mobile or B2C products
Tags & focus areas
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