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Fueling Data-Driven Logistics with Predictive Analytics

Technology – Pricing Optimization

Robust predictive pipelines for demand forecasting, resource optimization, and actionable fleet analytics.

+35%

Data Reliability

+40%

Analytical Speed

+50%

Decision Efficiency

89.2%

Model Accuracy

Pricing Optimization Impact

Advanced pricing optimization and data valorisation for mobility/logistics

Project Details

Client

Driiveme

Sector

Technology – Pricing Optimization

Year

2024

Key Technologies

XGBoostMLflowSHAPDatabricks

Mission

Lead Senior Data Scientist – Data Valorisation & Machine Learning

Pricing Optimization & Technology

Short mission (5 weeks) / Paris, France (hybrid)

Business needs audit and framing, advanced database exploration (Databricks, SQL). Data cleaning, missing values/outliers management, preprocessing scripts industrialization. Business-adapted feature engineering, analytical foundation preparation.

Contexte & Environnement

Driiveme, a mobility and logistics specialist, faced challenges in pricing optimization and data valorization. The complexity of multi-source and evolving data required rapid analytical capabilities and advanced machine learning models for operational decision-making.

Team

Lead Senior Data Scientist – Data Valorisation & Machine Learning

Environment

Databricks with SQL, MLflow tracking, Streamlit dashboards

Objectifs Cles

1XGBoost predictive models with MLflow tracking and optimization

2SHAP model explainability for full business transparency

3What-if simulations and interactive dashboard generation for decision-making

4+35% data reliability and +40% analytical exploitation speed

5Industrialized preprocessing scripts with business-adapted feature engineering

Technologies & Infrastructure

PythonPandasscikit-learnXGBoostSHAPSQLDatabricksMLflowStreamlitPowerBITableau

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