Braintrust

Fractional Data Scientist, Marketing Mix Modeling

San Jose, California, USA · full-time

Sponsorship not specifiedDetected 67 days ago

PythonFull-Stack DevelopmentDynamoDBAWSData EngineeringData ScienceComplianceHIPAACommunicationCollaboration

About 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.

Develop a methodology for defining Bayesian priors using historical data and, where applicable, localized "micro-holdout" / geo-testing variance.
Build logic to help estimate the contribution of different marketing channels using aggregate, privacy-safe data.
Generate model outputs that can support both executive-level median estimates and deeper analysis using confidence intervals.
Collaborate with the existing developer on how the model should connect to the broader product environment.
Model build, validation, documentation, and handoff to the development / dashboard team.
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.
Prior Calibration: Develop a methodology for defining Bayesian priors using historical data and, where applicable, localized "micro-holdout" / geo-testing variance.
Attribution & Budget Optimization Logic Channel Performance Modeling: Build logic to help estimate the contribution of different marketing channels using aggregate, privacy-safe data.
Probabilistic Reporting: Generate model outputs that can support both executive-level median estimates and deeper analysis using confidence intervals.
Technical Handoff: Collaborate with the existing developer on how the model should connect to the broader product environment.

Define the required input schema, expected output structure, assumptions, and limitations of the model.
Direct ownership of AWS infrastructure, Bedrock implementation, and dashboard development is not required for this role.
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.
Familiarity with AWS-based model deployment environments, especially SageMaker.
Experience with geo-testing, incrementality testing, or micro-holdout methodology.
Experience translating statistical outputs into plain-English insights or executive-facing recommendations.

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.

model selection, prior calibration, model logic, and output structure.
Scoping, framework recommendation, input/output schema definition, implementation plan, and estimated effort for Phase 2.
Work will be performed in a staging/dev environment with synthetic or de-identified data; no live production PHI access is expected.

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.

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 listing is sourced directly from Braintrust's careers page and normalized into a canonical job model.