Fractional Data Scientist – Marketing Mix Modeling

About the role

We are building a privacy‑first healthcare marketing attribution platform that replaces non‑compliant pixel tracking with server‑side, aggregate data analysis. As a Fractional Data Scientist you will design and implement the statistical engine that powers our Media Mix Modeling product, turning aggregated marketing data into actionable insights for budget allocation.

Key responsibilities

• Lead the design and implementation of an automated Bayesian MMM framework using tools such as Google Meridian, PyMC or NumPyro.
• Develop methods for calibrating Bayesian priors from historical and geo‑test data.
• Incorporate ad‑stock, decay, and non‑linear saturation effects to identify optimal spend thresholds.
• Define input schemas, output structures, assumptions and limitations for the model.
• Build privacy‑safe attribution logic that works with server‑side tracking and aggregate data.
• Provide probabilistic reporting with median estimates and confidence intervals.
• Package model code and documentation for seamless hand‑off to the engineering and dashboard teams.

Required profile

• Proven experience designing Bayesian MMM or similar marketing analytics models.
• Ability to work independently and collaborate with founders, developers and designers.
• Strong understanding of privacy‑first data aggregation and server‑side tracking concepts.

Required skills

• Python
• PyMC
• NumPyro
• Google Meridian (or similar Bayesian modeling frameworks)
• Meta Robyn (optional)
• AWS (serverless pipelines and infrastructure)