GAYASUDDIN

• Spearheaded end-to-end data analysis for a large-scale AI data annotation team, tracking KPIs such as Quality Score, AHT/NHT, Occupancy, Utilization, Task Completion Rate, and Precision Score, driving a 15–20% improvement in operational efficiency for Meta’s AI projects.
• Utilized Meta’s proprietary SRT annotation tool to extract and analyze annotator-level performance data, enabling accurate tracking of productivity and quality metrics across the team.
• Built and maintained SQL-powered dashboards using Power BI and Excel, enabling real-time performance monitoring that allowed leadership to identify bottlenecks and reduce task delays by 25%.
• Automated weekly and monthly reporting pipelines using Python (Pandas, NumPy), reducing manual reporting time by 35% and ensuring timely delivery of insights for stakeholder reviews.
• Delivered actionable recommendations that led to a 30% reduction in rejected/skipped tasks and a 12% improvement in average handling time (AHT) across the team.
• Partnered with Operations, QA, and Senior Managers to align insights with business goals, supporting SLA compliance and driving continuous process optimization.

• Developed end-to-end data analytics projects utilizing SQL, Excel, Power BI, and Python (Pandas, NumPy, Matplotlib, Seaborn, Plotly, Streamlit), delivering interactive and visual dashboards.
• Published all project work and code to GitHub, demonstrating a structured approach to learning and portfolio development.
• Performed in-depth exploratory data analysis on diverse datasets, including Global Terrorism Analysis and Vrinda Store Sales Analysis, to extract actionable business insights.
• Collaborated with mentors and peers to refine data storytelling techniques and solve complex real-world analytics problems.
• Gained comprehensive hands-on experience across the entire data analysis workflow, encompassing data cleaning, EDA, visualization, and insights communication.

1. Analyzed customer demographics, geography, and channel preferences to optimize marketing efforts. 2. Found women made ~65% of purchases, with top buyers aged 30–49. 3. Identified Maharashtra, Karnataka, and Uttar Pradesh as top-performing states. 4. Highlighted Amazon, Flipkart, and Myntra as the most-used order channels. 5. Recommended targeted ads/offers to women (30–49) in top states to boost revenue

1. Analyzed global terrorism incidents from 1970 to 2017, identifying Iraq and 2014 as the most impacted country and year respectively. 2. Identified Middle East, North Africa & South Asia as most affected regions. 3. Highlighted Bombing as the most frequent attack method and Taliban as the most active group 4. Built Streamlit dashboard to visualize attack trends, casualties, and targets types over time. 5. Proposed enhanced region-specific counter-terrorism strategies based on extracted insights.