• As a Marketing Science Product Engineer, you’ll work at the intersection of data science and engineering, transforming our MMM & causal frameworks into production-ready Python utilities. • Partner closely with the Director of Marketing Science - Product, marketing scientists, and engineering collaborators to turn statistical specifications into scalable, maintainable code. • Execute against Product-defined specifications. • Implement MMM modules such as Sequential Cross-Validation (SCV) utilities, Halo/cannibalization & revenue allocation logic, and more. • Build functions to automate Meridian pipelines. • Develop, automate and maintain metadata schemas for MMM model registry and versioning utilities.

• Work at the intersection of data science and engineering • Transform MMM & causal frameworks into production-ready Python utilities • Partner closely with the Director of Marketing Science - Product • Turn statistical specifications into scalable, maintainable code • Execute against Product-defined specifications • Collaborate with leadership to translate specifications into code • Write and maintain unit tests, regression tests, and reproducibility checks

• Implement MMM modules such as: Sequential Cross-Validation (SCV) utilities, Halo/cannibalization & revenue allocation logic, Parameter QC to detect overfitting, shrinkage misuse, leakage, etc. Scenario Planning engine, Science and Engineering for out-of-the-box attribution based on MMM variations. • Convert notebook based workflows into reusable and tested Python modules. • Build functions to automate Meridian pipelines. • Build statistical utilities for: Diminishing-return and saturation curves, Efficiency quadrants & spend allocation plots, Posterior diagnostic visualizations and stability checks. • Implement and support product-approved attribution methodologies. • Develop, automate and maintain: Metadata schemas for MMM model registry, Versioning utilities and standardized logs, Data audit & EDA standardization modules. • Integrate registry logging with Snowflake or other data stores. • Work with leadership to translate specifications into code. • Maintain clear documentation, examples, tests and onboarding material. • Support the enablement of measurement analysts using standardized utilities. • Serve as a technical connector between Product and the Marketing Science modeling team, ensuring new features are usable, scalable and aligned with methodology standards. • Help onboard modelers, enabling consistent usage of utilities, automated diagnostics, metadata logging and scenario tools. • Write and maintain unit tests, regression tests, and reproducibility checks for MMM packages and utilities. • Validate code performance across multiple brands and modeling conditions (different datasets, geographies, SKU structures, sales cycles). • Ensure SCV, diagnostics, metadata, and attribution modules are stable, performant, and free of methodological regressions. • Document edge cases and error handling recommendations.

• As a Marketing Science Product Engineer, you’ll work at the intersection of data science and engineering, transforming our MMM & causal frameworks into production-ready Python utilities used across teams and our UI environments. • You will partner closely with the Director of Marketing Science - Product, marketing scientists, and engineering collaborators to turn statistical specifications into scalable, maintainable code. • All decisions related to product scope, methodology changes, metrics definitions, acceptance criteria, and rollout timing remain with Product Leadership. • The role is expected to surface risks, ambiguities, or improvement opportunities, but not to independently redefine specifications. • Implement MMM modules such as: Sequential Cross-Validation (SCV) utilities, Halo/cannibalization & revenue allocation logic, Parameter QC to detect overfitting, shrinkage misuse, leakage, etc. • Scenario Planning engine, Science and Engineering for out-of-the-box attribution based on MMM variations. • Convert notebook based workflows into reusable and tested Python modules. • Build functions to automate Meridian pipelines. • Build statistical utilities for: Diminishing-return and saturation curves, Efficiency quadrants & spend allocation plots, Posterior diagnostic visualizations and stability checks. • Develop, automate and maintain: Metadata schemas for MMM model registry, Versioning utilities and standardized logs, Data audit & EDA standardization modules.