Data Scientist

Joined the data science team of an e-commerce platform operating in the retail sector to lead advanced analytics and data science projects at international scale.

• Led promotion efficacy analyses across 70+ retail brands, quantifying sales uplift, cannibalization, and return on investment to guide pricing and discount strategies across 10+ markets.
• Designed category-level forecasting models for weekly net sales covering 10 countries, 70+ brands, and 4,000+ stores, enabling data-driven demand planning and inventory optimization.
• Developed a custom AutoML pipeline orchestrating parallel training and inference across 234+ models, reducing time-to-production by 75%.
• Built interactive dashboards monitoring 234+ production forecasting models, providing real-time visibility into model performance and data drift while reducing troubleshooting time by 45%.
• Mentored junior data scientists, supporting technical integration and machine learning best practices.
• Technical environment: PySpark, MLflow, XGBoost, Python, Databricks, Power BI

Supported public and private sector clients as a data science and business intelligence consultant, leading data transformation and decision automation projects.

• Designed and delivered 3 comprehensive BI solutions including data warehouses and automated ETL pipelines for government and private sector clients, improving data quality and report reliability.
• Modeled dimensional schemas and optimized SQL queries, enhancing analytical performance for enterprise reporting workloads.
• Developed and automated ETL workflows via SQL Server Integration Services (SSIS), integrating data from multiple operational systems into centralized data warehouses.
• Built interactive dashboards in Power BI and SSRS, enabling business users to monitor key performance indicators and reducing manual reporting efforts.
• Developed and presented machine learning solutions as proofs of concept demonstrating predictive analytics capabilities for 2 major client engagements.
• Technical environment: SQL Server Integration Services (SSIS), SQL, Power BI, SSRS, Python

Joined the data science team of a medical device manufacturer to develop advanced predictive models supporting business strategy and asset optimization.

• Improved a SARIMAX time series demand forecasting model, increasing accuracy by 14% across all product lines during the unpredictable COVID-19 pandemic period.
• Introduced a refined customer segmentation model, identifying 3 key churn factors within Base and Sporadic cohorts, directly informing sales retention strategies for executive review.
• Segmented surgeon behavior over initial 12 months, forecasting volume, value, and lifetime value for acquisitions and conversions to optimize asset management strategies.
• Modeled customer transactions and lifetime value for sporadic and base segments, predicting churn probability and improving management of assets representing £75,000 capital.
• Technical environment: Python, SARIMAX, Scikit-learn, Pandas, NumPy

Conducted missions as an independent data analyst, developing custom predictive models for inventory management optimization and demand forecasting.

• Built custom predictive models in Python, including Planning For Every Part (PFEP), to forecast demand and improve inventory management.
• Optimized inventory management by aligning supply with demand and reducing stockouts.
• Introduced and taught data analytics principles to technical and non-technical audiences.
• Technical environment: Python, Scikit-learn, Pandas