Project: Marketing Mix Modeling (MMM) — Automation Backend (Service Industry Use Cases) Goal Develop a production-ready MMM engine that runs end-to-end in automation: data prep → model training → diagnostics → budget optimization → scheduled re-training. Deliver clean, well-tested code that can run headless (CLI/cron/Airflow/Step Functions), configured by YAML/JSON—no UI required. Scope of Work 1) Data layer (code, not pipelines) Implement schema-aware loaders that read canonical tables (daily/weekly): spend, sales, price, promo, optional reach_freq, search_query_volume. Provide transform utilities (carryover windows, seasonality/calendar features, holidays). Configurable data contracts (validate columns, grain, nulls, ranges) with clear errors. 2) MMM core modeling Implement Bayesian MMM with per-channel adstock/carryover and saturation (Hill/logistic). Support hierarchical pooling (brand/region) and priors; option for frequentist baseline. Optional bias controls for paid search (e.g., GQV/back-door controls). Robust uncertainty: posterior samples/intervals for ROAS and elasticities. 3) Calibration & validation Hooks to calibrate with lift/geo experiments (when provided). Time-split backtests; out-of-sample diagnostics; fit/forecast metrics. Automated model selection across adstock/shape families. 4) Budget optimization & what-if Deterministic optimizer (e.g., constrained nonlinear / Bayesian expected utility) that: maximizes expected sales/profit under budget & min/max channel constraints, supports diminishing returns and uncertainty (risk-aware plans). Fast what-if simulator given proposed spends; emits response curves & KPIs. 5) Automation & packaging CLI commands (examples below) and config-driven runs; no hard-coded paths. Idempotent outputs: model_bundle/ (parameters, curves, diagnostics) + plan/. Containerized (Docker) with reproducible environments; ready for CI. Comprehensive unit/integration tests; dataset fixtures (synthetic). 6) Documentation README + quick-start + config reference. Model math note (one pager) and interpretation guide (curves/ROAS/uncertainty).