Juan Sebastián Acevedo Rodríguez Email & Phone Number

Bogotá, Bogota, Co

Bogotá, Bogota, Co

Bogota, D.C., Capital District, Colombia

I collaborated with the Latam Logistics Team to improve driver retention and performance. Responsibilities included data analysis, KPI establishment, and dashboard development. Key involvement in devising strategies and conducting experiments, primarily using Python, PgSQL, Crontab, and Redash for task automation and database manipulation on cloud infrastructure. Notable projects include:- Implemented gamification for logistics partners, resulting in a 24% fleet growth. Integrated data from diverse databases like AWS RDS, Redshift, Redash, and Gsheets using Python. Transformed and presented data through external dashboards and automated emails. Analyzed cost-benefit and defined monetary incentives to contribute to the project's business aspects.- Conducted A/B testing across various LATAM cities to improve user experience, impact, and transparency of the driver bonus system. Developed backend infrastructure for data exposureand interacted with multiple APIs for massive communication delivery and database manipulation. Collaborated on experiment design and analyzed results using statistical methods, engaging various company departments such as product, prototype, and data warehousing

Bogota, D.C., Capital District, Colombia

- Developed an NLP algorithm to classify jobs into occupations according to the CUOC standard, based on their titles and descriptions. The algorithm performed a symmetric semantic search using sentence embeddings (SBERT - Hugging face), matching each job post to the most similar occupation in the CUOC database, and then performed a validation phase using the ChatGPT API. This project automated a task that previously required significant human effort and time, demonstrating the potential of AI in HR industry.- Implemented time series forecasting to estimate staffing needs for the upcoming year, using python libraries such as Prophet and Neuralprophet.

- Machine learning predictive model deployment for commercial operations.- Data Analysis and reporting.- Rotating shift optimization model.

- Machine Learning and Deep Learning models deployment to predict the bill demand.- Metaheuristics applied to Combinatorial Optimization solving the Machine Scheduling problem in the banknote printing.