As a Data Analyst at Grab, I apply my analytical skills to solve complex problems and support data-driven decision-making. I work with large datasets to identify trends, build predictive models, and create insightful visualizations that help optimize business processes. In addition to my core responsibilities, I am passionate about leveraging machine learning to tackle real-world challenges.Key Responsibilities:- Conduct data cleaning, processing, and analysis using SQL (presto), Python (pandas, pyspark, etc).- Design and implement predictive models (clustering, time-series forecasting) to predict trends, optimize services, and enhance - business performance.- Build interactive dashboards using Power BI to provide data insights to key stakeholders and senior management.- Collaborated with various departments to fulfill ad-hoc data analysis requests, ensuring accurate and relevant insights were delivered quickly to meet business requirements.- Automate data pipelines and improve reporting processes to support decision-making.Key Projects & Achievements:- Driver Gear Detection Model: Developed a detection model using YOLOv11 to analyze whether drivers are using the correct gear, fully equipped or not, enhancing operational safety and efficiency. This detection model processes vehicle data to identify gear usage, improving operational safety and reducing driving errors.- Contributed to the optimization of key business processes, resulting in increased operational efficiency.- Delivered data-driven insights that supported various departments and strategic decisions.

- Data Analysis: Work with large datasets to perform data processing, exploratory data analysis, and feature engineering. This involves extracting meaningful information from raw data to identify patterns, trends, and for the model development stage.- Model Development: Assist in developing predictive models using machine learning algorithms to assess risk, detect claim abuse cases in insurance, and optimize insurance products.- Collaboration: Collaborate with cross-functional teams, including Customer Relationship Management - CRM, New Business & Underwriting - NBU, and Claims team, to understand their requirements, data needs, and provide data-driven solutions to address business challenges.- Data Visualization: Create interactive and informative data visualizations using Power BI to communicate complex insights to various departments, including management and non-technical teams.