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# Lead Marketing Data Scientist

- **Company:** EPIC SEARCH PARTNERS
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $208,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Python (Programming Language)
- **Published:** July 21, 2026
- **Apply:** https://epicsearchpartners.com/job/lead-marketing-data-scientist-mmm/

## About the Role

* Graduate-level training in econometrics, statistics, economics, or another quantitative field.
 * Hands-on experience with Seemingly Unrelated Regression systems.
 * Proficiency with Bayesian probabilistic programming using PyMC, Stan, NumPyro, or an equivalent platform.
 * Strong knowledge of hierarchical modeling, partial pooling, and correlated time-series data.
 * Professional experience with marketing mix modeling, media measurement, attribution, or marketing science.
 * Strong Python skills and the ability to explain statistical findings to non-technical stakeholders.

Preferred:

 * PhD in economics, statistics, econometrics, or a related field.
 * Experience with a marketing science firm, econometric consultancy, media agency, or advanced MMM practice.
 * Experience with geo-lift, conversion-lift, or holdout experiments.
 * Familiarity with advertising platforms and APIs such as Meta, Google, DV360, or The Trade Desk.

WHO WILL THRIVE HERE

You want to build something rather than maintain someone else's system. You care about the statistical architecture behind marketing measurement, have opinions about aggregation bias, and are comfortable owning important modeling decisions.

## Description

You will architect, build, and operate a proprietary marketing measurement framework based on Bayesian Seemingly Unrelated Regressions, hierarchical estimation, and adaptive media structuring., * Build a Bayesian SUR framework using PyMC, Stan, NumPyro, or an equivalent platform.

 * Develop hierarchical models and partial-pooling structures across channels, campaigns, creatives, audiences, and placements.
 * Implement adstock, saturation, carryover, and synergy transformations.
 * Develop multi-KPI analysis across awareness, engagement, intent, and conversion.
 * Establish rigorous model validation, including posterior predictive checks, rolling holdouts, experimental calibration, and coefficient-stability diagnostics.
 * Produce media contribution estimates, saturation curves, marginal efficiency analysis, and budget optimization scenarios.
 * Translate complex findings into clear recommendations for clients and media teams.
 * Maintain and evolve the framework as new clients, channels, and data sources are added.


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