April 2026 (2026-04) focused on stabilizing model refresh workflows and strengthening validation/serialization for VariableScaling in pymc-marketing. The work delivered a robust, user-controlled scaling mechanism and the groundwork for multi-dimensional, polymorphic scaling with improved test coverage.

March 2026 performance summary focusing on business value and technical outcomes across two repositories. Key enhancements centered on documentation clarity for marketing analytics workflows and readability improvements in PyMC core models. - pymc-labs/pymc-marketing: Added documentation guidance to require including the date in lift test rows when time_varying_media is enabled, ensuring calibration maps to the correct temporal multiplier and improving model accuracy for time-varying media in marketing mix models. Commit: c3aefe95b65824084eb4a569b4e8a4b32296cc89 (docs: clarify lift test date alignment for time-varying media (#2360)). - pymc-devs/pymc: Enhanced string representation and LaTeX formatting for PyMC models and distributions, including better handling of named variables and LaTeX support for data variables. Commit: e90fba940ab2c24af4f063832830217addd1fe34 (Improve model text representation). Overall impact: Improved calibration reliability and model readability, enabling faster adoption and more accurate reporting for marketing mix analyses and PyMC-based modeling. Technologies/skills demonstrated: documentation best practices, Python/PyMC development, LaTeX formatting integration, model representation improvements, cross-repo collaboration.

February 2026 was concentrated on delivering a production-ready Geo-Level Marketing Mix Model Calibration Notebook for pymc-marketing, enabling calibration of a multidimensional MMM using geo-level lift test results. The deliverable improves parameter recovery and model accuracy while providing practical workflows for geo-level experiments and impact analysis. It includes explanatory content, enhanced visualizations, and is now part of the docs gallery to ease adoption by teams across marketing analytics and data science.

November 2025 monthly summary for pymc-labs/pymc-marketing. Focused on improving lift test documentation and user experience. Delivered Lift Tests Documentation and UX Improvements with clearer explanations, improved information organization via a dropdown, and links to relevant resources. Also clarified that users do not need to manually scale data, streamlining the user experience. No major bugs fixed this month; effort centered on documentation, UX, and maintainability to reduce onboarding friction and support load. Impact includes better user understanding, smoother adoption of lift tests, and more maintainable docs. Technologies/skills demonstrated include technical writing, UX/UI design within notebooks, cross-repo documentation practices, and collaborative development.

July 2025 monthly summary for pymc-labs/pymc-marketing: Delivered advanced counterfactual analysis capabilities by integrating CounterfactualSweep into the MMM model, enabling sensitivity analyses and marginal effects across marketing channels. Updated documentation, examples, and tests to support the new capabilities, improving onboarding and QA coverage. No major bugs reported; efforts focused on robust feature delivery, code quality, and maintainability.

December 2024 monthly work summary focused on delivering a new customer-centric modeling capability within the pymc-marketing repository, along with practical examples to accelerate adoption.

In 2024-11, I delivered a critical bug fix in the pymc-marketing module to improve the numerical accuracy and data representation of the counterfactual analysis pipeline. This change directly enhances the reliability of counterfactual predictions used for marketing analytics, reducing the risk of misinformed campaign decisions and ROI estimates. The fix is tracked under issue #1175 and implemented in commit 26d6af8d32a28b223f2606e53d5d732198e24f75, reinforcing model integrity and overall system stability.