Versatile and impact driven Senior Data Scientist with a PhD in Statistical Learning and over 15 years of experience solving complex buisness problems across banking, insurance, and logistics sectors. Skilled in applying data analytics to uncover key trends and patterns, driving strategic business decisions. Proven ability to deliver impactful results in dynamic environments. Fluent in English and Spanish. Eligible to work in the UK.
Python, R, SQL, SAS, machine learning, statistical modeling, data visualization, econometrics, forecasting etc.
Company/Client: Pacifico Bank
Role: Senior Data Scientist
Problem Statement: Optimizing branch and ATM locations to improve customer service and profitability.
Methodology: Spatial statistics, optimization models, web scraping (government data, competitor locations), geocoding, SQL.
Results & Impact: Increased customer accessibility, improved profitability.
Company/Client: Pacifico Bank
Role: Senior Data Scientist
Problem Statement: Increasing credit card usage through tailored loyalty benefits.
Methodology: Machine learning (K-means, DBSCAN, Random Forest, Gradient Boosting), Python (Scikit-learn, TensorFlow), Power BI dashboards.
Results & Impact: Increased credit card usage.
Company/Client: SABMiller
Role: Demand planner
Problem Statement: Reducing logistics costs and improving delivery efficiency.
Methodology: Multi-level operational forecasting, machine learning, Python, SQL, Airflow, AWS Redshift, Apache Kafka.
Results & Impact: 20% reduction in logistics costs, 15% increase in sales.
Company/Client: SABMiller
Role: Demand planner
Problem Statement: Predicting the impact of an unprecedented price change on demand.
Methodology: Time-series analysis, econometric modeling, Python.
Results & Impact: Demand prediction with an absolute error of 20,000 hectoliters.
Company/Client: SABMiller
Role: KPI's analyst
Problem Statement: Reducing the impact of extra-route orders on delivery costs.
Methodology: Geolocation validation algorithm, Python, geospatial libraries.
Results & Impact: Measurable decrease in delivery costs.
Company/Client: Latina Insurance
Role: Statistician
Problem Statement: Optimizing pricing strategies to maximize profit.
Methodology: Statistical modeling, machine learning (clustering, classification), Python.
Results & Impact: 5% increase in annual profit.