— Regional Workforce Lead Forecasting & Analytics Automation Champ
I lead workforce planning across multi-country operations — turning forecasts, schedules, and real-time signals into service-level stability.
Numbers behind the work — across countries, accounts, and the skills that hold it together.
In planning & operations
From Amazon audit to Regional Workforce Lead — a continuous arc through data, ops, and leadership.
Countries managed simultaneously
End-to-end scheduling & capacity planning across two national markets in parallel.
Accounts under active planning
Forecasts, schedules, and resource allocation tuned per client SLA and seasonal pattern.
Specialised skills, daily
From Python & VBA automation to Lean Six Sigma — a stack chosen to make operations measurable.
Remote · 1 year 8 months
Supported Amazon's launch in Egypt by auditing large datasets for the data-entry pipeline. Learned what data integrity looks like at scale — the foundation everything else was built on.
Cairo · 11 months
Pivoted into BPO operations. Resolved complex customer issues with analytical rigour and clear communication — the first lessons in how front-line work shapes the metrics planners later optimise.
Cairo · 9 months
Owned real-time operations and SLA adherence for the call center. Led the team end-to-end — performance, development, scheduling, leaves. Where dashboards became decisions, in minutes.
Cairo · 2 yrs 2 mo
Led capacity planning across two countries and six+ accounts. Built Python forecasting models that fold machine learning into the planning cycle. Owned the full scheduling stack — from intent to implementation to optimisation.
Cairo · ongoing
Promoted to lead workforce management across the region. Multi-site, multi-country scope. Drive planning accuracy, streamline WFM processes, and hold service-level stability — where the discipline of planning meets the breadth of regional leadership.
Three layers — strategy, data, and the human work of leading change.
The frameworks that make operations measurable.
The tools that turn intent into decisions.
Where models meet people.
I don't just schedule shifts. I model the system that makes the shifts possible — the demand patterns, the agent skills, the seasonal noise, the SLA cliffs. Then I build the forecasts, the automations, and the dashboards that let the whole operation run a step ahead, instead of a step behind.
Real-time fires happen because something upstream wasn't modelled right. I start every problem with the demand-supply structure beneath it.
If a planner runs the same query every Monday, that query becomes a Python job. Time saved on plumbing is time spent on judgement.
Forecasts that flatter no one. Dashboards that show the gap, not hide it. Lean Six Sigma discipline applied to the data itself.
Capacity for two national markets, six+ accounts, in one cohesive model. Resource allocation tuned per account SLA, seasonality, and skill mix. The model isn't perfect — but it's transparent enough that the ops floor can see where it's wrong and tell me why.
Forecasting models written in Python — regression and statistical methods stitched together for business planning that doesn't surprise anyone in week three. Outputs feed directly into the schedule engine, replacing what used to be week-long spreadsheets.
As Senior RTM I owned SLA adherence in the moment — but the lasting work was turning that minute-by-minute knowledge into structural insights for the planning layer. Bottlenecks identified at real-time speed inform capacity assumptions at planning speed.
Open to conversations about workforce planning, forecasting at scale, BPO ops, or any place where data, scheduling, and Lean discipline meet.
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