Data Scientist, Marketing Analytics and Marketing Mix Modeling

GrowTal builds artificial intelligence and machine learning products for marketing measurement. Our platform connects a client's marketing and revenue data, measures performance, models what drives outcomes, and reports the results.
We run per-customer marketing mix models in Google Meridian today, hand-specified per client. This role converts that into a self-serve marketing mix modeling feature in VibeMA: a system that specifies, fits, validates, and grades a model for any qualifying account without an analyst in the loop. This role reports to the Chief Technology Officer.
Responsibilities
Generalize the existing per-customer Meridian implementation into a multi-tenant system that runs unattended.
Automate control selection: candidate screening, ranking on detrended correlation, and a full-sampling convergence check before any control is added or dropped.
Automate prior setting, including the media and baseline split, which currently requires a per-client judgment call that out-of-sample metrics cannot adjudicate.
Automate modeling window selection, including detection of structural breaks and of the point where an upstream metric becomes available.
Automate per-channel identifiability diagnostics: detect channels whose contribution is prior-driven or unstable across holdout seeds, and surface that state in the product rather than reporting a point estimate.
Own holdout design, including leakage from carry-over and from a baseline fit on both sides of a held-out point.
Own the model acceptance gate: out-of-sample error, convergence and divergence thresholds, degenerate baseline detection, credible interval width, and tier assignment.
Automate data sufficiency gating that determines whether an account can be modeled at all.
Own data quality assertions on curated inputs and the tests that prove each assertion fires.
Define the normalized output schema, and how contributions, uncertainty, and assumptions are presented to end users.
Establish quality monitoring and regression detection across accounts and successive refits.
Work with engineering on runtime, cost, and refit cadence at volume.
Requirements
Experience building and shipping marketing mix models against real marketing spend, where the output informed budget decisions.
Experience systematizing modeling work into a repeatable automated pipeline rather than analyst-run one-off engagements.
Fluency with Bayesian sampler diagnostics: R-hat, divergences, posterior geometry, and why convergence pathologies do not reliably reproduce at reduced sampling.
Ability to reason about identification, including collinearity, low-variance regressors, and telling a data-driven contribution from a prior-driven one.
Holdout and validation design for time series with carry-over effects.
Ability to translate analytical judgment into automated diagnostics and acceptance criteria that hold without human review.
Calibration against incrementality or geographic lift experiments where available.
Understanding of where platform-reported and last-click attribution mislead, and how modeling and experimentation address that gap.
Strong Python and SQL, and production-quality code that runs unattended on a schedule.
Ability to present modeled results and uncertainty to non-technical end users without overstating confidence.
Fluency with artificial intelligence assisted tooling, and full accountability for your methodology.
Preferred
Direct experience with Google Meridian.
Hierarchical or pooled modeling across many accounts.
Incrementality or geographic lift experiment design.
Bayesian workflow at scale, or probabilistic programming beyond a single modeling framework.
Familiarity with major advertising and analytics platform data models.
Experience productizing analytics for non-analyst end users.
Stack
Python, SQL
Google Meridian
BigQuery, Funnel.io, Looker
Postgres on Cloud SQL
Google Cloud Platform: Cloud Run
Anthropic software development kit
Linear, Notion

Work arrangement
Yes