Quote Pricing Optimization

Mission

Price optimization: model the acceptance probability of a quote (accepted/not accepted) and recommend the optimal margin-maximizing price. Classification & scoring: XGBoost (logistic) to estimate acceptance propensity per quote. What-if simulations: scenario engine varying price from –99% to +100% to explore the acceptance rate ↔ margin trade-off and plot optimum points by segment. Data pipelines & quality: cleaning, transformations, normalization/standardization, leakage detection and removal, strict temporal split. Robust validation: temporal cross-validation (Time Series CV), train/test monitoring and generalization safeguards. Reasoned Auto-ML: Optuna / Hyperopt for hyperparameter tuning (Bayesian/Tree-Parzen search). Interpretability & trust: SHAP, LIME, local & global importances, explanation reports for business teams. Performance monitoring: metrics dashboards (LogLoss, ROC-AUC, PR-AUC and calibration), data/model drift monitoring. Productionization: model packaging, deployment (batch/API), documentation and usage playbooks.

Contexte & Environnement

Business scope: analysis of quotes with multiple explanatory criteria (product, customer, sales context, etc.) to balance price vs. acceptance probability. Stakes: provide sales/finance teams with a price simulator and actionable recommendations (price corridors, inflection points) to increase margin without hurting conversion. Constraints: methodological rigor (no information leakage), time as a structuring variable (TS-CV), strong explainability for business adoption. Stakeholders: Sales / Revenue / Finance, Data, IT; cross-functional Data Scientist role with decision-makers.

Objectifs Cles

Technologies & Infrastructure