I'm a Computer Science graduate (Alexandria University, 2025) who sits at the intersection of three disciplines — business intelligence, data science, and backend engineering.
My sharpest edge is call center analytics: I've worked with live ACD feeds, built Power BI dashboards that supervisors use every day, and written SQL to compute Adherence, Occupancy, and SLA in real time.
On the data science side I apply machine learning — churn prediction, time-series forecasting, and agent segmentation — and embed the outputs back into dashboards that non-technical teams can actually use.
My backend exposure covers .NET, Angular, MongoDB, and Hadoop, which means I understand how data systems are built, not just queried.
Building the plumbing that data flows through. REST APIs with .NET, scalable data pipelines with Hadoop, infrastructure automation, release pipelines, and frontend clients with Angular.
Turning raw numbers into predictive models. Churn classification, time-series forecasting with LSTM & ARIMA, customer segmentation, and regression for engagement optimization.
Real-time call center monitoring, SLA tracking, and executive dashboards. Specialized in contact center KPIs — AHT, Adherence, Occupancy, CSAT — with Power BI and Tableau.
Worked with real call center ACD data to build a 15-minute-refresh Power BI dashboard displaying live agent states (Available, ACW, Break, Aux), queue SLA status, and intraday AHT. Replaced a manual Excel tracker used by floor supervisors. SLA breach escalation time dropped 18%.
Analyzed 6 months of real agent-level call data (50k records) profiling AHT, CSAT, and schedule adherence. K-Means clustering segmented agents into performance tiers. Delivered Power BI report and Excel summary for team leads.
Random Forest classifier on 70k telecom records. 91% accuracy, 0.89 AUC-ROC. Engineered 15 features including support call frequency and usage ratios. Model outputs embedded in a Power BI retention dashboard for business teams.
Time-series forecasting on 5 years of daily stock data for 10 companies. LSTM achieved 3.2% MAPE, outperforming ARIMA baseline by 22%. Visualized prediction intervals with Plotly for non-technical stakeholders.
Cleaned 50k+ rows of multi-table sales data via SQL CTEs and Power Query. 5-page Power BI report with DAX for YoY comparisons, revenue by region, product, and rep. Identified top 3 revenue-driving categories.
Designed and developed responsive web applications using HTML, CSS, JavaScript, and Angular during a formal internship at ITI. Applied UX/UI principles and full Agile SDLC workflow within a supervised engineering team.
Collected 6 months of platform data via APIs. Built a regression model predicting engagement rate (R² = 0.78). Recommendations implemented by client, growing average post reach by 25% in the first month.
I'm actively looking for full-time roles in BI analytics, data science, or backend engineering. If you're building a data team or a contact center analytics platform — I want to talk.