Paola Wong

Paola Wong

About

Detail

DATA SCIENTIST | ANALYTICS, BI & AI SOLUTIONS
Provincia de San José, Costa Rica
Contact Paola regarding: 
work
Full-time jobs
Starting at USD4k/month
Flexible work
Starting at USD25/hour
groups
Networking

Timeline

work
Job
school
Education
folder
Project

Résumé

Jobs verified_user 0% verified
  • B
    Data & AI Lead
    BOSS.Tech
    Mar 2023 - Nov 2024 (1 year 9 months)
    Led the creation and implementation of the Data & AI department's foundation, including data governance and cybersecurity policies. Promoted data democratization, ensuring responsible and secure accessibility through role-based access models with AWS IAM. Designed and deployed Machine Learning and Deep Learning models using TensorFlow, PyTorch, and cloud services like AWS SageMaker. Automated sprint task management through integrated workflows in Zapier and Python, improving planning efficiency by 30%. Implemented solutions using AWS ECS, Docker, and ETL pipelines with data from Django Backend. Conducted educational sessions on cybersecurity, covering phishing, spear phishing, and best digital security practices. Designed advanced OpenAI in
  • Lanshore
    Developer | Tester
    Lanshore
    Feb 2022 - Mar 2023 (1 year 2 months)
    Contributed to testing frameworks and troubleshooting processes for SAP Commissions. Supported project teams with data integration tasks related to commissions systems. Assisted in maintaining operational consistency across commissions-related workflows, leveraging statistical analysis, R programming, and advanced mathematical techniques to optimize processes.
  • Hakkoda
    Associate Data Engineer
    Hakkoda
    Sep 2021 - Dec 2021 (4 months)
    Worked on data pipelines with Snowflake to enable seamless data integration and processing. Developed and implemented machine learning models to detect early-stage lung cancer, leveraging advanced image analysis techniques. Conducted data preprocessing and feature extraction using Python, ensuring high quality datasets for training and testing. Collaborated with cross-functional teams to integrate predictive models into existing systems, optimizing diagnosis workflows. Researched and applied deep learning algorithms for medical imaging tasks, enhancing accuracy and reliability in predictions.
  • Unilever
    Internship - Data Engineer public Remote experience
    Unilever
    May 2021 - Sep 2021 (5 months)
    - Process Automation: Transformed manual process by implementing Power Platform solutions Developed Power Automate workflows for CAM e-commerce teams - Commercial Optimization: Analyzed e-commerce performance using Python Proposed data-driven SKU strategies adopted across Central American markets - Change Management: Trained non-technical users (Marketing, Sales, Admin) on Power Platform tools Created documentation to support adoption of new systems -Technical Implementation: Built Power BI dashboards for sales and e-commerce tracking Developed predictive models for sales analysis Engineered data pipeline connecting raw OLAP cubes to operational databases, transforming unstructured sales data into analyzable formats for CAM markets
  • A
    AI Developer and Researcher public Remote experience
    Ainnovatech
    May 2021 - Sep 2021 (5 months)
    - Developed computer vision models (CNN architectures in TensorFlow) for diabetic retinopathy diagnosis - Processed medical imaging datasets using OpenCV, Python libraries - Documented research findings for clinical validation
Education verified_user 0% verified
  • PROMIDAT
    Machine Learning Expert
    PROMIDAT
    Jan 2023 - Feb 2024 (1 year 2 months)
  • L
    Data Science Engineer
    LEAD University
    Jan 2020 - Current (6 years 4 months)
Projects (professional or personal) verified_user 0% verified
  • 3
    3.Sprint Automation
    Mar 2023 - Nov 2024 (1 year 9 months)
  • 2
    2.AI Patent
    Mar 2023 - Nov 2024 (1 year 9 months)
  • I
    Identity Synchronization
    Mar 2023 - Nov 2024 (1 year 9 months)
    Developed a model to identify and synchronize user data across multiple third-party platforms integrated with the BOSS.Tech app (such as Zendesk, QuickBooks) even when data was incomplete or poorly formatted. Improved match accuracy by 86% and streamlined cross-platform data consistency. Created an advanced machine learning model that identifies patterns in unstructured or incomplete data, including job titles, names, and email details, improving user matching processes by over 40%. This innovative model, which integrates natural language processing and parallel computing techniques, is currently in the patenting process in the United States, highlighting its market-leading potential. Implemented an automated workflow for task planning, red