Responsibilities

• 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: Develop a methodology for defining Bayesian priors using historical data and, where applicable, localized “micro-holdout” / geo-testing variance.
• Advanced Modeling: Incorporate adstock effects, decay, and non-linear saturation curves to help identify optimal spending thresholds across marketing channels.
• Model Inputs / Outputs: Define the required input schema, expected output structure, assumptions, and limitations of the model.
• Attribution & Budget Optimization Logic
• Channel Performance Modeling: Build logic to help estimate the contribution of different marketing channels using aggregate, privacy-safe data.
• Privacy-Safe Attribution: Account for server-side tracking, Conversion API context, and aggregate attribution models where traditional click-level identifiers are unavailable.
• Budget Optimization: Define how the model should identify saturation points, flag diminishing returns, and recommend budget reallocations.
• Probabilistic Reporting: Generate model outputs that can support both executive-level median estimates and deeper analysis using confidence intervals.
• Handoff Structure: Package the modeling logic and outputs in a way that can be consumed by the existing developer and dashboard designer.

Requirements

• Deep experience in Marketing Mix Modeling, marketing attribution, marketing science, or advanced media measurement.
• Strong experience with Bayesian inference, probabilistic modeling, or applied statistical modeling.
• High proficiency in Python and experience with MMM / probabilistic modeling frameworks such as Meridian, Robyn, PyMC, NumPyro, or similar.
• Experience working with aggregate attribution models, server-side tracking data, Conversion API context, or privacy-safe marketing measurement.
• Ability to translate complex statistical outputs into clear, actionable business recommendations.
• Ability to define model inputs, outputs, assumptions, priors, and limitations.
• Comfortable scoping ambiguous technical requirements in a startup-style environment.
• Strong written communication skills for asynchronous collaboration.

Preferred

• Experience with North American healthcare marketing, especially US-based healthcare marketing, is a strong differentiator. Familiarity with healthcare marketing channels, patient acquisition, or regulated healthcare advertising is highly valuable.
• Familiarity with AWS-based model deployment environments, especially SageMaker.
• Experience building models that feed SaaS dashboard or reporting products.
• Experience with geo-testing, incrementality testing, or micro-holdout methodology.
• Experience translating statistical outputs into plain-English insights or executive-facing recommendations.