Fractional Data Scientist, Marketing Mix Modeling, Valparaíso

Company Braintrust is a global talent network that connects top independent professionals with leading companies for high-quality, flexible work.
We help organizations hire skilled talent faster while giving professionals access to vetted opportunities with innovative teams.
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: 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.
2. Attribution not part of the core scope for this role Ideal Candidate Background 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.
Nice-to-Haves 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.
Engagement Structure Phase 1: Scoping, framework recommendation, input/output schema definition, implementation plan, and estimated effort for Phase
2. Phase 2: Model build, validation, documentation, and handoff to the development / dashboard team.
Data Access: Work will be performed in a staging/dev environment with synthetic or de-identified data; no live production PHI access is expected.
Success Metric A successful engagement results in a defensible Bayesian MMM / attribution modeling framework that provides marketers with a reliable "single source of truth" for channel performance, even when traditional click-level identifiers are removed for compliance reasons.
The model should be clearly documented, practical to maintain, and structured so the existing development team can integrate it into the broader product experience.