Developing predictive models to forecast student engagement and success using historical data: • Focusing on the creation and training of machine learning models. • Concentrating on data collection, exploratory data analysis, feature selection, model development, and validation. • Focus on using time series analysis, regression models, or other suitable machine learning techniques to predict engagement trends accurately. Cleaning, preprocessing, and transforming data to prepare it for model training and evaluation: • Emphasize data handling and preparation tasks essential for model development. Monitoring and evaluating the performance of machine learning models: • Highlight the application of statistical methods for performance monitoring using statistical methods to ensure accuracy and efficiency. Designing and refining prompts for various language models (LLMs) to ensure they provide accurate, relevant, and contextually appropriate responses for educational content: • Focusing on the development and optimization of language model prompts. Evaluating the performance of different language models: • Emphasize the evaluation and analysis of language model outputs using quantitative and qualitative metrics. Mentoring high school level students in AI and Data Analysis based project development. • Upon request, mentoring advanced students in developing AI based projects • Upon request, mentoring advanced students in analyzing and understanding data Having meetings with team members: • Attend regular check-in meetings with team members to discuss updates, milestones, and any issues.

Providing comprehensive support to students enrolled in Artificial Intelligence courses, helping both remotely and within on-campus facilities. Resolved students' day-to-day challenges related to AI concepts and algorithms, fostering a deeper understanding of the subject matter

Conducted comprehensive business analysis on change requests, projects, and production issue s; identified key areas for improvement and presented datadriven solutions that reduced customer support issues by 50%.Achievements• Implemented Python scripts to automate testing processes, which results in a 40% reduction in incident count over 6 months. • Developed and implemented innovative data modeling techniques to convert raw data into actionable insights for 15+ clients leading in a 35% boost in company productivity.• Conducted extensive code review sessions and identified areas for improvement, which culminated in a 20% decrease in code errors and improvedapplication stability. • Analysed data on escalations over the past year and recommended changes to the application vendor selection process, leaving in a decline of 25% in overall escalations.• Collaborated with cross-functional teams to analyse user behaviour data, leading to the development of new features that raised user engagement by 15%.Key Engagements• Ensure coverage and support of all critical service management process: Incident Management, Problem Management, Change Manag ement, Application Enhancements• Managing escalations by involving Application vendors, Business representatives, and IT support teams.• Implemented employee training programs that resulted in a significant reduction of errors by 15%, leading to improved productivity and customer satisfaction.• Received an appreciation badge for managing the highest number of client-reported incidents and enhancing team efficiency by 30%