Marketing Science Product Engineer

Marketing Science Product Engineer We are a tech-enabled growth firm–at the intersection of marketing, consulting & data intelligence–igniting revenue and brand recognition for leading and emerging companies around the world. As a people-first firm, we value diversity in backgrounds and experiences and believe our people and culture are key to our success. We are hyper-focused on helping brands drive revenue growth and brand recognition, ultimately delivering irrefutable value for our clients. Overview 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. This is not an analytics role; it is a product builder role for causal measurement in Python. The role is fully defined by Product Leadership, executing against product-defined specifications while surfacing risks, ambiguities, or improvement opportunities. Responsibilities Model Engineering & Python Development Implement MMM modules such as Sequential Cross-Validation utilities, Halo/cannibalization & revenue allocation logic, Parameter QC for overfitting detection, shrinkage misuse, leakage checks, and a Scenario Planning engine 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, standardized logs, and 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. Surface feedback and implementation risks but do 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, and 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 % of methodology specifications delivered as reusable modules. Target: >90% of new specs implemented within agreed timeframes. Reusable Automation Reuse rate of modules across client models. Target: >90% of active MMM projects use the standardized utilities. QA & Stability Test coverage + regression success rate. Target: >80% code test coverage and 90% feature adoption among modelers and