***Proficiency in spoken and written English at an advanced level is required for this role.

A day in the life:

As a Marketing Science Product Engineer, youll 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. This is not an analytics role; it is a product builder role for causal measurement in Python. This role executes against Product-defined specifications.

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.

Responsibilities:

Model Engineering & Python Development:

- 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.

Causal Measurement & Feature Development:

- 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.

Workflow Automation & Registry:

- 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.

Collaboration & Documentation:

- 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.

Bridge Between Methodology, Product, and Modelers:

- 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.
- This role surfaces feedback and implementation risks but does not independently define or modify product logic, methodology, metrics or rollout decisions.

Testing, QA & Reliability:

- 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.

Role Requirements:

• 5 years in data science, data engineering and machine learning engineering.
• Very strong proficiency in Python, including:
  
  • • Modular package design & clean function architecture
    • Pandas, NumPy, SciPy, serialization, project organization
    • Versioning, environment management, testing frameworks
• Experience building time-series and causal models.
• Familiarity with Media Attribution and Bayesian modeling.
• Comfortable working in notebook + package development workflows.
• Experience building reusable plotting and BI utilities such as Looker Studio, Matplotlib, Plotly and Seaborn.
• Prior work with SQL, Snowflake or GCP, ideally with Google Cloud Run.
• Strong communication skills for explaining model mechanics and code behavior.

Key Performance Indicators (KPIs)

Methodology Code
- KPI: % of methodology specifications delivered as reusable modules
- >90% of new specs implemented within agreed timeframes

Reusable Automation
- KPI: Reuse rate of modules across client models
- Target: >90% of active MMM projects use the standardized utilities

QA & Stability
- KPI: Test coverage + regression success rate
- Target: >80% code test coverage and <10% post-release bugs

Bridge Communication
- KPI: Feature adoption + feedback resolution cycle
- Target: >90% feature adoption among modelers + <15-day turnaround for prioritized feedback

Governance Adherence
- KPI: % of deliverables aligned with Product-approved scope and acceptance criteria
- Target: 100% of releases approved by Product prior to documentation or rollout

Most Important Things (MITs)

• Turn methodology into code: translate statistical rules into reusable, production ready Python modules.
• Build automation & reusable utilities: not one-off notebooks; scalable functions for audits, validation, diagnostics, attribution and planning.
• Ensure formal testing & QA: unit tests, reproducibility checks, and regression testing to guarantee stability across models and datasets.
• Bridge methodology, product & modelers: Implement Product-approved specifications in code, gather structured feedback from modelers and surface improvement proposals for Product review.