- Design and develop Android apps using Kotlin and Jetpack Compose. - Create cross-platform apps for Android and iOS with Flutter. - Build intuitive, responsive, and user-friendly UI/UX designs. - Collaborate with stakeholders to gather and analyze project requirements. - Translate requirements into technical specifications and development tasks. - Write clean, maintainable, and efficient code in Kotlin, Jetpack Compose, and Flutter. - Implement platform-specific features for Android and iOS. - Conduct unit, integration, and UI testing to ensure high-quality deliverables. - Debug and resolve performance and functionality issues promptly. - Optimize app performance for various devices and OS versions. - Monitor app analytics and address crashes or performance issues. - Use Git for version control and codebase management. - Work independently or collaboratively based on project needs. - Integrate RESTful APIs and third-party libraries for added functionality. - Ensure secure communication between apps and backend services. - Test apps for smooth functionality across devices and operating systems. - Utilize Flutter for efficient cross-platform development and deployment. - Provide post-deployment support for bug fixes, feature updates, and compatibility. - Conduct code reviews and refactoring to enhance app maintainability. - Create and update technical documentation for development processes. - Report project progress to clients with actionable updates. - Stay updated on Kotlin, Jetpack Compose, Flutter, and mobile development trends. - Explore new technologies and tools to improve user experience. - Prepare apps for deployment on Google Play Store and Apple App Store. - Manage app updates, reviews, and feedback from users.

- Imported, cleaned, and processed large datasets from various sources (CSV files, MongoDB, SQLite).- Built data visualizations and conducted correlation analysis to explore relationships between variables.- Developed predictive models using linear regression, logistic regression, decision trees, random forest, gradient boosting, ARMA, and GARCH.- Created data pipelines for missing value imputation and categorical feature encoding.- Enhanced model performance through techniques such as hyperparameter tuning, reducing overfitting, and addressing imbalanced data.- Utilized the Linux command line for data processing and management.- Conducted chi-square tests for hypothesis testing in A/B experiments.- Built k-means clustering models for customer segmentation and used principal component analysis (PCA) for data visualization.- Developed interactive dashboards and data applications using Plotly Dash.- Implemented custom Python classes for ETL processes and built custom APIs to serve model predictions.- Evaluated model performance with precision and recall metrics and identified and addressed data biases to prevent discrimination.