Fractional Data Scientist, Marketing Mix Modeling (Santiago)

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Job description

About the Project

We are developing a privacy-first healthcare marketing attribution platform designed to solve the “tracking gap” created by evolving HIPAA and OCR regulations. As traditional 1:1 pixel tracking and Multi‑Touch Attribution become non‑compliant, our platform provides healthcare organizations with a server‑side, aggregate‑data approach to measuring channel performance.

We are seeking a Marketing Attribution Data Scientist to architect the modeling layer of a scalable Media Mix Modeling product. You will be responsible for building the statistical “brain” that translates aggregate marketing and performance data into actionable strategic insights.

The engineering team will handle the serverless data pipelines, AWS infrastructure, dashboard, and UI. This role will focus on the mathematical engine: model selection, prior calibration, model logic, and output structure.

The Role

You will lead the design and implementation of an automated Bayesian MMM framework. Your goal is to create the modeling foundation that helps marketers understand channel performance, identify diminishing returns, and make smarter budget allocation decisions after traditional click‑level identifiers are removed.

You will collaborate with the founder, existing full‑stack developer, and dashboard designer to ensure the model outputs can be handed off cleanly for dashboard and reporting implementation.

Key Responsibilities

1. Bayesian Model Architecture
- Framework Implementation: Evaluate and recommend a robust Bayesian MMM framework, with preference for Google Meridian, PyMC, NumPyro, or similar. Familiarity with other MMM tools such as Meta Robyn is a plus, but not required.
- Prior Calibration Postúlate en Kit Empleo: kitempleo.cl/empleo/1dy4ax