Mathieu Su

Context: cross-functional role between Data, Shop, Customer Experience, Supply, Ops, and Tech to analyze customer/professional journeys, ensure data reliability, and deliver operational internal tools.

• Product and business A/B tests: scoping, instrumentation, and analysis of tests on boosted slots, display frequency, anti-churn calls, price changes, and conversion funnels; definition of populations, control groups, KPIs, observation windows, and decision rules.

• Statistical analysis of experiments: comparison of control/treatment, significance reading, analyses by country, region, service, frequency, weekday/time slot, and customer segment; reporting of impacts on order rate, conversion, retention, churn, recurring, and GMV.

• Pricing analytics: analysis of price changes, household price history, detection of effects by segment, and construction of a prediction/monitoring logic to anticipate sensitive areas or services before deployment.

• Customer churn models: construction of BigQuery/Python training datasets, feature engineering on order history, complaints, support conversations, future orders/potentials, recency, frequency, and GMV; comparison of models and production of prioritized lists.

• Design of a PostgreSQL database to centralize data from multiple sources as part of an IT POC.

• Extraction, cleaning, transformation, and injection of data using Python, Psycopg2, and SQLAlchemy.

• Facilitation of business workshops to define needs, clarify business rules, and validate the quality of delivered data.

Environment: PostgreSQL, Python, Psycopg2, SQLAlchemy, SQL.

• Migration of business dashboards to Power BI and monitoring of performance indicators for operational teams.

• SQL extraction, creation of analytical tables, cleaning, profiling, and automation of reporting with Power Automate.

• Predictive modeling to estimate the volume of new prospects based on the budget invested per marketing channel.

• Collaboration with sales, marketing, and IT teams to translate business needs into actionable analyses and dashboards.

Environment: SQL, Power BI, Python, Pandas, scikit-learn, Plotly, Power Automate.