Kenneth Ibea Email & Phone Number

Mississauga, Ontario, Canada

- Designed and implemented Transformer models for financial vulnerability prediction and marketing strategies, leveraging attention mechanisms to improve accuracy by 30%.- Developed NLP models using BERT and GPT for text classification and sentiment analysis, enhancing customer insights and engagement by 20%.- Conducted data cleaning and processing using Python and Apache Spark to handle large datasets and ensure high-quality data input for models.- Engineered features using embedding techniques like Word2Vec, GloVe and tokenization for efficient text representation.- Managed cloud deployments on AWS, optimizing system performance by 25% and ensuring robust and scalable infrastructure.- Created interactive Tableau dashboards to visualize model predictions, aiding in data-driven decision-making.- Developed AI agents for automated customer service, improving response times and customer satisfaction by 15%.

Chicago, Illinois, United States

- Developed LSTM models for sequential data analysis in recommendation systems, improving recommendation accuracy by 25%.- Built NLP models for customer satisfaction analysis using LSTM and Transformer models like BERT, leveraging customer feedback data to enhance service quality and user experience.- Performed extensive data preprocessing and feature engineering using NLTK and spaCy for efficient text processing.- Architected real-time, interactive reporting features using Tableau, facilitating better decision-making processes.- Deployed scalable machine learning models on AWS using Docker and Kubernetes, ensuring efficient and reliable service delivery.- Conducted sentiment analysis with BERT, enhancing insights into customer feedback and satisfaction.

Singapore, Singapore

- Developed CNN models for document classification, increasing accuracy by 20% for legal document categorization.- Implemented RNN models for sequential data analysis, enhancing prediction capabilities in legal case outcomes.- Engineered ETL pipelines using Azure Data Factory, managing over 500,000 customer data points in Snowflake.- Utilized Python and SQL for comprehensive data cleaning and preprocessing, improving data quality for analysis.- Created interactive Tableau dashboards to visualize legal analytics, reducing report generation time by 15%.- Developed data pipelines with Apache Kafka and Spark Streaming, improving data processing speed by 20%.- Created AI agents for automating legal document review, reducing manual processing time by 30%.

Manila, National Capital Region, Philippines

- Developed and validated machine learning models for customer segmentation using k-means clustering and decision trees, improving targeting and marketing efficiency.- Created predictive models for business analysis using logistic regression and random forests, supporting strategic decision-making with data-driven insights.- Performed data cleaning, processing, and feature engineering using Python to ensure high-quality data for modeling.- Participated in full-stack web development using AngularJS and .NET, integrating APIs such as Google Maps and Stripe.- Created dynamic dashboards in Power BI, utilizing DAX and Power Query to provide actionable business insights.- Built AI models for customer segmentation and personalized marketing, enhancing campaign effectiveness by 20%.

Master'S Degree, Data Science

Bachelor'S Degree, Computer Science