I transform operational and customer data into actionable business insights. My work focuses on operations, demand, pricing and performance analytics, combining data analysis, decision support and systems thinking to help teams make faster, better decisions.
- Python: Pandas, NumPy, Jupyter
- SQL: querying, validation, data checks
- Excel & Power Query: pivot tables, dashboards, KPI analysis
- Data Visualisation: Matplotlib, Seaborn
- Operational Analytics: performance monitoring, KPI reporting
- Automation & Systems: Airtable, Make.com, workflow design
Business problem: Retailers must balance product availability against waste for short shelf‑life items.
Solution: Analysed sales, pricing, promotions, weather and wastage data to uncover demand patterns and support better inventory planning.
Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, Jupyter.
Business problem: Static pricing and poor demand planning can reduce margins and increase waste.
Solution: Built a decision engine using historical demand and wastage patterns to recommend pricing or supply actions.
Tools: Python, Pandas, SQLite, Airtable API.
Business problem: Credit control teams needed to reduce low‑value calls and focus on revenue‑generating activity.
Solution: Used operational call data and KPI analysis to assess whether Twilio improved efficiency, call outcomes and potential time recovery.
Tools: Excel, pivot tables, KPI analysis, dashboard design.
Business problem: E‑commerce teams need to understand whether customers return after their first purchase.
Solution: Performed cohort analysis to measure retention patterns and compare customer behaviour over time.
Tools: Python, Pandas, Seaborn, Jupyter.
Business problem: Healthcare operations need better visibility into admissions, flow and forecasting.
Solution: Built a structured analytics pipeline covering data extraction, SQL validation, dataset building and predictive modelling.
Tools: Python, SQL, Pandas, Matplotlib, Jupyter.
Research problem: Multi‑agent swarms need to coordinate under limited communication (latency, packet loss) in 6G environments.
Approach: Developed a simulation framework to test how communication constraints affect exploration, information propagation and convergence in distributed networks.
Tools: Python, NumPy, Matplotlib.
Below is an example dashboard created during the Twilio Impact analysis:
